https://www.slicer.org/w/api.php?action=feedcontributions&user=Lauren&feedformat=atomSlicer Wiki - User contributions [en]2024-03-29T08:38:22ZUser contributionsMediaWiki 1.33.0https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerDcm2nii&diff=61171Documentation/Nightly/Extensions/SlicerDcm2nii2019-05-25T16:57:41Z<p>Lauren: Created page with "<noinclude>{{documentation/versioncheck}}</noinclude> <!-- ---------------------------- --> {{documentation/{{documentation/version}}/module-header}} <!-- --------------------..."</p>
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Extension: [[Documentation/{{documentation/version}}/Extensions/SlicerDcm2nii | SlicerDcm2nii]]<br><br />
Acknowledgments:<br />
This work is supported in part by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through the following grants:<br />
* NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research)<br />
* NIH P41EB015898 (National Center for Image-Guided Therapy)<br />
* NIH P41EB015902 (Neuroimaging Analysis Center)<br />
* National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.<br />
Major Contributors: Isaiah Norton, Lauren J. O'Donnell, Steve Pieper, Fan Zhang (Brigham and Women's Hospital)<br><br />
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License: [http://www.slicer.org/pages/LicenseText Slicer License]<br />
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Website: [http://slicerdmri.github.io SlicerDMRI]<br />
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|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:NCIGT-Logo.jpeg|NCIGT<br />
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SlicerDcm2nii builds and distributes Chris Rorden's dcm2niix (https://github.com/rordenlab/dcm2niix) as part of the Slicer superbuild.<br />
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This enables loading of DICOM diffusion-weighted images (DWI) into Slicer.<br />
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* SlicerSlicerDMRI organization page on github: https://github.com/SlicerDMRI<br />
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[[Category:Documentation/{{documentation/version}}/Modules/Diffusion]]<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDownsample&diff=59111Documentation/Nightly/Modules/TractographyDownsample2018-06-16T14:23:31Z<p>Lauren: Created page with "The purpose of this module is to reduce the amount of tractography data for more rapid visualization and/or analysis. Especially for high resolution diffusion MRI data, points..."</p>
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<div>The purpose of this module is to reduce the amount of tractography data for more rapid visualization and/or analysis. Especially for high resolution diffusion MRI data, points may be spaced very closely along fiber tracts. While this is important for computation of the fiber tracts for best anatomical accuracy, it is not necessary for visualization or most quantitative analyses. For example, if points are spaced every .2 mm along a fiber tract, this will be much slower to visualize than a fiber tract with points spaced every 1mm. In general, point spacing for visualization can be similar to the voxel size of accompanying image data (for example 1mm or 2mm).<br />
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This module can remove excess points or fibers (polylines) from a tractography (vtkPolyData) object. It aims for an output point spacing requested by the user. For example if input point spacing is 0.4mm, and the user requests 1mm, the module will find the largest possible point spacing under 1mm, which will be .8mm. Therefore about half of the points will be removed (0.4mm spacing to 0.8mm spacing). The rest of the points will be preserved. Also, both endpoints of each fiber will be preserved.</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/4.8/Training&diff=56955Documentation/4.8/Training2017-10-24T18:24:46Z<p>Lauren: /* Slicer4 Neurosurgical Planning Tutorial */</p>
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<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
=Introduction: Slicer {{documentation/version}} Tutorials=<br />
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*This page contains "How to" tutorials with matched sample data sets. They demonstrate how to use the 3D Slicer environment (version {{documentation/version}} release) to accomplish certain tasks. <br />
*For tutorials for other versions of Slicer, please visit the [[Training| Slicer training portal]].<br />
*For "reference manual" style documentation, please visit the [[Documentation/{{documentation/version}}|Slicer {{documentation/version}} documentation page]]<br />
*For questions related to the Slicer4 Training Compendium, please send an e-mail to '''[http://www.na-mic.org/Wiki/index.php/User:SPujol Sonia Pujol, Ph.D., Director of Training of 3D Slicer.]'''<br />
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* Some of these tutorials are based on older releases of 3D Slicer. The concepts are still useful but bear in mind that some interface elements and features will be different in updated versions.<br />
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__TOC__<br />
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=General Introduction=<br />
<br />
==Slicer Welcome Tutorial==<br />
{|width="100%"<br />
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*The [[media:SlicerWelcome-tutorial_Slicer4.5.pdf|SlicerWelcome tutorial]] is an introduction to Slicer based on the Welcome module.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want a general introduction to the software.<br />
*Modules: Welcome to Slicer, Sample Data<br />
*Based on: 3D Slicer version 4.6<br />
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[[image:SlicerWelcome-image.png|250px|SlicerWelcome tutorial]]<br />
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==Slicer4Minute Tutorial==<br />
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*The [[media:Slicer4.5minute_SoniaPujol.pdf|Slicer4Minute tutorial]] is a brief introduction to the advanced 3D visualization capabilities of Slicer 4.5.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want to discover Slicer in 4 minutes.<br />
*Modules: Welcome to Slicer, Models<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:Slicer4minute.zip|Slicer4Minute dataset]] contains an MR scan of the brain and 3D reconstructions of the anatomy<br />
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[[image:Slicer4minute-image.png|250px|right|Slicer4Minute tutorial]]<br />
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==Slicer4 Data Loading and 3D Visualization ==<br />
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*The [[Media:3DDataLoadingandVisualization_Slicer4.5_SoniaPujol.pdf | Data loading and 3D visualization]] course guides through the basics of loading and viewing volumes and 3D models in Slicer4 . <br />
*Author: Sonia Pujol, Ph.D.<br />
*Modules: Welcome to Slicer, Sample Data, Models.<br />
*Audience: End-users<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualizationData.zip | 3DVisualization dataset]] contain an MR scan and a series of 3D models of the brain.<br />
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[[Image:Slicer4DataLoading_tutorial.png|right|250px|]]<br />
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=Tutorials for software developers=<br />
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== Slicer4 Programming Tutorial ==<br />
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*The [https://www.dropbox.com/s/wrhrvvmplosiis1/Slicer4_ProgrammingTutorial_SPujol-SPieper_Nightly.pdf?dl=0# Slicer Programming tutorial] guides through the integration of a python module in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Steve Pieper, Ph.D.<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.7<br />
*The [https://www.dropbox.com/s/6yxu8qepmvywk0n/HelloPython_Nightly.zip?dl=0 HelloPython dataset] contains sample data set (MR scan of the brain) and complete Python module examples.<br />
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[[Image:HelloPythonTutorial.png|right|250px|]]<br />
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For additional Python scripts examples, please visit the [[Documentation/{{documentation/version}}/ScriptRepository|Script Repository page]]<br />
<br />
==Developing and contributing extensions for 3D Slicer==<br />
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*The [http://goo.gl/IP4cdg Developing and contributing extensions for 3D Slicer tutorial ] is an introduction to the internals of 3D Slicer and the process of contributing a 3D Slicer extension.<br />
*Authors: Andrey Fedorov, Jean-Christophe Fillion-Robin, Steve Pieper<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.4<br />
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[[Image:Contributing3DSlicerExtension.png|right|250px|]]<br />
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=Specific functions=<br />
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==Slicer4 Diffusion Tensor Imaging Tutorial ==<br />
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*Please visit [http://dmri.slicer.org/docs/ dmri.slicer.org/docs] for the latest documentation of SlicerDMRI.<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/DiffusionMRIanalysis.pdf Diffusion Tensor Imaging Tutorial] course guides through the basics of loading Diffusion Weighted images in Slicer, estimating tensors and generating fiber tracts. <br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: End-users and developers<br />
*Modules: Data, Volumes, DWI to DTI Estimation, Diffusion Tensor Scalar Measurements, Editor, Markups,Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.6<br />
*The [[media:Dti tutorial data.zip|DTI dataset]] contains an MR Diffusion Weighted Imaging scan of the brain.<br />
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[[Image:Slicer4DTI Tutorial.png|right|250px|]]<br />
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==Slicer4 Neurosurgical Planning Tutorial==<br />
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*Please visit [http://dmri.slicer.org/docs/ dmri.slicer.org/docs] for the latest documentation of SlicerDMRI.<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/WhiteMatterExplorationTutorial.pdf Neurosurgical Planning tutorial] course guides through the generation of fiber tracts in the vicinity of a tumor.<br />
*Author: Sonia Pujol, Ph.D., Ron Kikinis, M.D.<br />
*Audience: End-users and developers<br />
*Modules: Volumes, Editor, Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.6<br />
*The [[Media:WhiteMatterExplorationData.zip| White Matter Exploration datasets]] contains a Diffusion Weighted Imaging scan of brain tumor patient.<br />
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[[Image:NeurosurgicalPlanningTutorial.png|right|250px|link=http://vimeo.com/67336069]]<br />
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==Slicer4 3D Visualization of DICOM images for Radiology Applications==<br />
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*The [[Media:3DSlicer_Dicom_RSNA2015_SoniaPujol.pdf |3D Visualization of DICOM images for Radiology Applications]] course guides through 3D data loading and visualization of DICOM images for Radiology Applications in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Kitt Shaffer, M.D., Ph.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 visualization capabilities.<br />
*Modules: DICOM, Volumes, Volume Rendering, Models.<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualization_DICOM_images_part1.zip | 3DVisualizationDICOM_part1]] and [[Media:3DVisualization_DICOM_images_part2.zip | 3DVisualizationDICOM_part2]] datasets contain a series of MR and CT scans, and 3D models of the brain, lung and liver.<br />
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[[Image:Slicer4RSNA_2.png|right|250px|]]<br />
|}<br />
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==Slicer4 Quantitative Imaging tutorial==<br />
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*The [[media:QuantitativeImaging_Slicer4.5.pdf | Slicer4 Quantitative Imaging tutorial]] guides through the use for Slicer for quantifying small volumetric changes in slow-growing tumors, and for calculating Standardized Uptake Value (SUV) from PET/CT data.<br />
*Authors: Sonia Pujol, Ph.D., Katarzyna Macura, M.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 quantitative imaging capabilities.<br />
*Modules: Data, Volumes, Models, Change Tracker, PET Standard Uptake Value Computation<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:QuantitativeImaging.zip| Quantitative Imaging dataset]] contains a series of MR and PET/CT data.<br />
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[[Image:Slicer4_QuantitativeImaging.png|right|250px|]]<br />
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== Slicer4 IGT ==<br />
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*[http://www.slicerigt.org/wp/user-tutorial/ Slicer IGT tutorials]<br />
*Authors: Tamas Ungi, M.D, Ph.D., Junichi Tokuda, Ph.D.<br />
*Audience: End-users interested in using Slicer for real-time navigated procedures. E.g. navigated needle insertions or other minimally invasive medical procedures.<br />
*Modules: SlicerIGT Extension<br />
*Based on: Slicer4.3.1-2014.09.14<br />
*Data: [https://onedrive.live.com/redir?resid=7230D4DEC6058018!2937&authkey=!AGQkSCZOwjVYXw8&ithint=folder%2cpptx Slicer-IGT datasets]<br />
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[[Image:SlicetIGT.png|right|150px|]]<br />
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== Slicer4 3D Printing ==<br />
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* The video tutorial [https://youtu.be/Uht6Fwtr9hE Segmenting a CT for 3D Printing of a Lumbar Phantom] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.7.<br />
** Author: Hillary Lia<br />
** Audience: Users and developers interested in 3D printing<br />
* The [https://www.slicer.org/wiki/Documentation/4.6/Training#Segmentation_for_3D_printing Segmentation for 3D printing] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.6.<br />
** Author: Csaba Pinter, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
* This ''Slicer 4.3 [https://www.youtube.com/watch?v=MKLWzD0PiIc 3D printing tutorial]'' shows how to prepare 3D Slicer data for 3D printing using legacy Editor module.<br />
** Authors: Nabgha Farhat, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
|align="right"|[[Image:20170717_3DPrintingTutorialYoutube.PNG|280px]]<br />
|}<br />
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== Slicer4 Image Registration ==<br />
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*The [https://www.slicer.org/slicerWiki/index.php/File:RegistrationTutorial_3DSlicer4.5_spujol.pdf Registration tutorial] shows how to perform intra- and inter-subject registration within Slicer.<br />
* Authors: Sonia Pujol, Ph.D., Dominik Meier, Ph.D., Ron Kikinis, M.D.<br />
* Audience: Users and developers interested in image registration<br />
* Dataset: [[Media:RegistrationData.zip| 3D Slicer Registration Data]]<br />
|align="right"|[[File:registration_Slicer4.png|250px]]<br />
|}<br />
*Based on: 3D Slicer version 4.5<br />
See [[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|the Registration Library for worked out registration examples with data]].<br />
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== Fast GrowCut ==<br />
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* The [[media:FastGrowCutTutorial.pdf |Fast GrowCut tutorial]] shows how to perform a segmentation using the Fast GrowCut effect in Slicer.<br />
* Authors: Hillary Lia<br />
* Audience: Users interested in segmentation<br />
|align="right"|[[File:FastGrowCutLogo.png|200px]]<br />
|}<br />
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==Slicer4 Radiation Therapy Tutorial ==<br />
** The [https://app.assembla.com/spaces/slicerrt/subversion/source/HEAD/trunk/SlicerRt/doc/tutorials/SlicerRT_TutorialIGRT_4.7.pdf?_format=raw SlicerRT tutorial] is an introduction to the Radiation Therapy functionalities of Slicer.<br />
** Author: Csaba Pinter, Andras Lasso, An Wang, Gregory C. Sharp, David Jaffray, Gabor Fichtinger. <br />
** Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip download] from MIDAS server<br />
**Based on Slicer 4.7<br />
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== Other ==<br />
<br />
Additional (non-curated) videos-based demonstrations using 3D Slicer are accessible on [http://www.youtube.com/results?search_query=3d+slicer&sm=3 You Tube].<br />
<br />
= 3D Slicer Tutorial contests=<br />
<br />
==Winter 2017 Tutorial contest==<br />
<br />
===Segmentation for 3D printing===<br />
{|width="100%"<br />
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*The [https://www.assembla.com/spaces/slicerrt/documents/bmRQGEzzur54v-dmr6CpXy/download/bmRQGEzzur54v-dmr6CpXy Segmentation for 3D printing Tutorial] is an introduction to the new [[Documentation/{{documentation/version}}/Modules/SegmentEditor|Segment Editor]] module, demonstrated through the popular topic of 3D printing. <br />
*Author: Csaba Pinter (Queen's University, Canada)<br />
* [https://www.youtube.com/watch?v=Uht6Fwtr9hE Narrated video version on YouTube].<br />
*Dataset: [[:File:BasePiece.zip|Phantom base STL model]] Source: [http://perk-software.cs.queensu.ca/plus/doc/nightly/modelcatalog/ PerkLab].<br />
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[[File:SlicerWinterProjectWeek2017-Segmentation-for-3d-printing.png | 200px]]. <br />
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===Slicer Pathology===<br />
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*The [[Documentation/{{documentation/version}}/Extensions/SlicerPathology|Slicer Pathology Tutorial]] describes how to use the corresponding tools for automatic and semi-automatic pathology image segmentation.<br />
*Author: Erich Bremer (Stonybrook), Andriy Fedorov (Brigham and Women’s Hospital)<br />
*Dataset: Available directly with the Slicer Pathology Slicer extension.<br />
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[[File:SlicerPathologyScreenShot8.png | 200px]]. <br />
|}<br />
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===Simple Python Tool for Quality Control of DWI data===<br />
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*The [http://www.na-mic.org/Wiki/images/3/3a/SimpleDiffusionGradientInformationExtractorTutorial_Chauvin_Jan2017.pptx Simple Multi-shell Diffusion Gradients Information Extractor Tutorial] describes how to use a simple Python script for parsing multi-shell sensitizing gradients information from nifti file format (separated bvecs, bvals files).<br />
*Author: Laurent Chauvin (ETS Montreal)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SimpleDiffusionGradientInformationExtractorTutorial.png | 200px]]. <br />
|}<br />
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===SPHARM-PDM===<br />
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*The [https://www.nitrc.org/docman/view.php/308/1982/SPHARM-PDM_Tutorial_July2015.pdf SPHARM-PDM Tutorial] describes how to use SPHARM-PDM and ShapePopulationViewer Slicer extensions to respectively compute point-based models using a parametric boundary description for the computing of Shape Analysis and perform the quality control between the different models.<br />
*Author: Jonathan Perdomo (UNC), Beatriz Paniagua (Kitware Inc.)<br />
*Dataset: [https://www.nitrc.org/docman/view.php/308/1981/SPHARM_Tutorial_Data_July2015.zip Tutorial Data]<br />
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[[File:SlicerWinterProjectWeek2017-SPHARM-PDM.png | 200px]]. <br />
|}<br />
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===Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink===<br />
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*The [https://www.na-mic.org/Wiki/images/a/ab/ROSIGTLTutorial_Tokuda_Jan2017.pptx Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink Tutorial] describes the software architecture of surgical robot systems and allows to acquire hands-on experience of software-hardware integration for medical robotics.<br />
*Author: Junichi Tokuda (Brigham and Women’s Hospital)<br />
*Dataset: Not available.<br />
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[[File:SlicerWinterProjectWeek2017-Integration-ROS-3DSlicer-OpenIGTLink.png | 200px]]. <br />
|}<br />
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===Fiber Bundle Volume Measurement===<br />
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*The [http://www.na-mic.org/Wiki/images/5/57/Fiber_Bundle_Volume_Measurement.pptx Fiber Bundle Volume Measurement Tutorial] aim is to calculate the volume of the fiber bundle that passes through the Corpus Callosum(CC). Following this tutorial, you’ll be able to (1) convert fiber bundles to label map and (2) calculate volume measurements from the fiber bundles.<br />
*Author: Shun Gong (Shanghai Changzheng Hospital, China)<br />
*Dataset: [http://www.na-mic.org/Wiki/images/4/4c/FiberVolume_data.zip Tutorial data]: The following data are provided: Baseline image, Down sampled whole brain tractography (conducted as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]] and down-sampled to about 10000 fibers using Tractography Display module), Corpus callosum label map (drawn as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]]).<br />
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[[File:SlicerWinterProjectWeek2017-FiberBundleVolumeMeasurements.png | 200px]]. <br />
|}<br />
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==Winter 2016 Tutorial contest==<br />
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===Subject Hierarchy===<br />
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*The [http://wiki.na-mic.org/Wiki/images/2/27/SubjectHierarchy.TutorialContestWinter2016.pdf Subject Hierarchy] tutorial demonstrates the basic usage and potential of Slicer’s data manager module Subject Hierarchy using a two-timepoint radiotherapy phantom dataset.<br />
*Author: Csaba Pinter, Queen's University, Canada<br />
*Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip SlicerRT_WorldCongress_TutorialIGRT_Dataset] The tutorial dataset is a two-timepoint phantom dataset taken from a RANDO head&neck phantom. It contains two studies, the planning one is a DICOM study consisting of a CT grayscale image and radiotherapy data: contours, dose distribution, treatment beams, plan information. The second timepoint consists of a CT NRRD volume and a dose NRRD volume.<br />
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[[File:SubjectHierarchyTutorial.png | 200px]]. <br />
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===Fiber Bundle Selection and Scalar Measurements===<br />
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*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/FiberBundleSelectionAndScalarMeasurement.pdf Fiber Bundle Selection and Scalar Measurements] tutorial guides through the use of the Diffusion Bundle Selection module and the Fiber Tract Scalar Measurement module for diffusion MRI tractography data analysis.<br />
*Author: Fan Zhang, University of Sydney Australia and Brigham and Women's Hospital<br />
*Dataset: [[media:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016.zip| Fiber Bundle Selection And Scalar Measurement Tutorial Dataset]]<br />
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[[File:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016_Snapshot.png|200px]]<br />
|}<br />
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===Plastimatch ===<br />
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*The [http://www.na-mic.org/Wiki/images/5/5c/Plastimatch_TutorialContestWinter2016.pdf Plastimatch tutorial] guides through registration and wrapping of DICOM and DICOM-RT data using the Plastimatch extension of 3D Slicer.<br />
*Author: Gregory Sharp, Massachusetts General Hospital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:Plastimatch_TutorialContestWinter2016.zip Plastimatch Tutorial Dataset]<br />
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[[File:PlastimatchTutorial_Winter2016Contest.png|200px]]<br />
|}<br />
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===UKF ===<br />
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*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/UKFTractography.pdf UKF tutorial] guides through the use of the Unscented Kalman Filter (UKF) tractography module. <br />
*Author: Pegah Kahali, Brigham and Women's Hopital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:UKF-Tractography_TutorialContestWinter2016.zip UKF tutorial Dataset]<br />
|align="right"|<br />
[[File:UKF_Winter2016.png|200px]]<br />
|}<br />
<br />
==Summer 2014 Tutorial contest== <br />
<br />
===Cardiac Agatston Tutorial===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:TutorialContest_CardiacAgatstonScoring_2014.pdf Cardiac Agatston Scoring Tutorial]<br />
*Authors: Jessica Forbes, Hans Johnson, University of Iowa<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CardiacAgatstonMeasures_TutorialContestSummer2014.zip Cardiac Agatston Scoring Tutorial Dataset]<br />
|align="right"|<br />
[[File:CardiacAgatstonMeasuresModuleScreenshot.jpg| 250px]]<br />
|}<br />
<br />
===CMR Toolkit LA workflow===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflow_TutorialContestSummer2014.pdf CMR Toolkit LA Workflow Tutorial]<br />
*Authors: Salma Bengali, Josh Cates, University of Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflowData_TutorialContestSummer2014.zip CMRToolkitLAWorkflow Dataset]<br />
|align="right"|<br />
[[Image:Utah_SummerContest2014_tutorial.png|300px]]<br />
|}<br />
<br />
==Summer 2013 Tutorial contest==<br />
<br />
===Cardiac MRI Toolkit===<br />
{|width="100%"<br />
|<br />
*[[Media:Cardiac MRI Toolkit Tutorial Summer2013.pdf|Cardiac MRI Toolkit]]<br />
*Authors: Salma Bengali, Josh Cates, SCI, Utah<br />
*Dataset: [[Media:Cardiac_MRI_Toolkit_Tutorial_Data.zip|Cardiac MRI Toolkit Tutorial Dataset]]<br />
|align="right"|<br />
[[Image:CMRToolkit_Tutorial_Image.png|250px]]<br />
|}<br />
<br />
===HelloCLI===<br />
{|width="100%"<br />
|<br />
*[[Media:Hello_CLI_TutorialContestSummer2013.pdf|HelloCLI]]<br />
*Authors: Nadya Shusharina, Greg Sharp, MGH, Boston<br />
*Dataset: [[Media:Hello_CLI_TutorialContestSummer2013.zip|HelloCLI Dataset]]<br />
|align="right"|<br />
[[Image:Cli_icon.png|300px]]<br />
|}<br />
<br />
===SlicerRT===<br />
{|width="100%"<br />
|<br />
*[[Media:SlicerRT_TutorialContestSummer2013.pdf|SlicerRT Tutorial]]<br />
*Authors: Csaba Pinter, Andras Lasso (Queen's), Kevin Wang (PMH, Toronto)<br />
*Dataset: [[Media:CsabaPinter-SlicerRtTutorial_Namic2013June.zip|SlicerRT Dataset]] <br />
|align="right"|<br />
[[Image:667px-SlicerRT_0.10_IsocenterShiftingEvaluation.png|250px]]<br />
|}<br />
<br />
===DTIPrep===<br />
{|width="100%"<br />
|<br />
*[[Media:DTIPrep_TutorialContestSummer2013.pdf|DTIPrep]]<br />
*Authors: Dave Welch, SINAPSE, IOWA <br />
*Dataset: [[Media:DTIPrepData_TutorialContestSummer2013.zip|DTIPrep Dataset]]<br />
|align="right"|<br />
[[Image:DTIPrep-tutorial.png|250px]]<br />
|}<br />
<br />
== Summer 2012 Tutorial contest == <br />
<br />
===Automatic Left Atrial Scar Segmenter ===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/CARMA-LA-Scar_TutorialContestSummer2012 Automatic Left Atrial Scar Segmenter] <br />
*Authors: Greg Gardner, Josh Cates, SCI, Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CARMA-LA-Scar_TutorialContestSummer2012.zip CARMA-LA-Scar data]<br />
|align="right"|<br />
[[Image:Carma afib auto scar.png|250px]]<br />
|}<br />
<br />
===Qualitative and quantitative comparison of two RT dose distributions===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:PlastimatchDose_TutorialContestSummer2012.pdf Qualitative and quantitative comparison of two RT dose distributions]<br />
*Authors: James Shackleford, Nadya Shusharina, Greg Sharp, MGH<br />
|align="right"|<br />
[[Image:PlastimatchDose.png|250px]]<br />
|}<br />
<br />
===Dose accumulation for adaptive radiation therapy===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:DoseAccumulationforAdaptiveRadiationTherapy_TutorialContestSummer2012.pdf Dose accumulation for adaptive radiation therapy]<br />
*Authors: Kevin Wang, Csaba Pinter, Andras Lasso, PMH, Queen's<br />
|align="right"|<br />
[[Image:AdaptiveradiationTherapy.png|250px]]<br />
|}<br />
<br />
===WebGL Export===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:WebGLExport_TutorialContestSummer2012.pdf WebdGLExport]<br />
*Authors: Nicolas Rannou, Daniel Haehn, Children's Hospital<br />
|align="right"|<br />
[[Image:WebGLExport.png|250px]]<br />
|}<br />
<br />
===OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*[http://wiki.slicer.org/slicerWiki/images/f/f1/OpenIGTLinkTutorial_Slicer4.1.0_JunichiTokuda_Apr2012.pdf OpenIGTLink]<br />
*Authors: Junichi Tokuda, BWH<br />
|align="right"|<br />
[[Image:OpenIGTLink.png|250px]]<br />
|}<br />
<br />
=Additional resources =<br />
{|width="100%"<br />
|<br />
* This ''Slicer 4.1 [http://vimeo.com/41096643 webinar]'' presents the new features and improvements of the release, and a brief overview of work for the next release.<br />
* Authors: Steve Pieper Ph.D.<br />
* Audience: First time users and developers interested in Slicer 4.1 new features.<br />
* Length: 0h20m<br />
|align="right"|[[Image:Webinar-Slicer-4.1.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*This ''Intro to Slicer 4.0 [http://vimeo.com/37671358 webinar]'' provides an introduction to 3DSlicer, and demonstrates core functionalities such as loading, visualizing and saving data. Basic processing tools, including manual registration, manual segmentation and tractography tools are also highlighted. This webinar is a general overview. For in depth information see the modules above and the documentation pages.<br />
*Authors: Julien Finet, M.S., Steve Pieper, Ph.D., Jean-Christophe Fillion-Robin, M.S. <br />
*Audience: First time users interested in a broad overview of Slicer’s features and tools.<br />
*Length: 1h20m<br />
|align="right"|[[Image:Webinar.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*The ''[[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|Slicer Registration Case Library]]'' provides many real-life example cases of using the Slicer registration tools. They include the dataset and step-by-step instructions to follow and try yourself. <br />
:Author: Dominik Meier, Ph.D.<br />
:Audience: users interested learning/applying Slicer image registration technology<br />
|align="right"|[[Image:RegLib_table.png|250px|link=http://wiki.slicer.org/slicerWiki/index.php/Documentation/{{documentation/version}}/Registration/RegistrationLibrary]]<br />
|}<br />
<br />
= External Resources =<br />
<br />
== Murat Maga's blog posts about using 3D Slicer for biology ==<br />
<br />
* [https://blogs.uw.edu/maga/2017/04/11/getting-started-with-3d-slicer-as-a-biologist/ Slicer for Biologists]<br />
* [https://blogs.uw.edu/maga/2017/04/11/a-worked-example-getting-and-visualizing-data-from-digimorph/ Loading data from DigiMorph]<br />
* [https://blogs.uw.edu/maga/2017/04/11/morphosource-data-and-dealing-with-dicom-series-in-slicer/ Fixing problem DICOM]<br />
* [https://blogs.uw.edu/maga/2017/04/12/scissors-tool-is-awesome/ Scissors tool is awesom]<br />
<br />
== Using the (legacy) Editor ==<br />
<br />
This set of tutorials about the use of slicer in paleontology is very well written and provides step-by-step instructions. Even though it covers slicer version 3.4, many of the concepts and techniques have applicability to the new version and to any 3D imaging field:<br />
<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial.html Open Source Paleontologist: 3D Slicer: The Tutorial]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-ii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part II]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part III]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iv.html Open Source Paleontologist: 3D Slicer: The Tutorial Part IV]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-v.html Open Source Paleontologist: 3D Slicer: The Tutorial Part V]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-vi.html Open Source Paleontologist: 3D Slicer: The Tutorial Part VI]<br />
<br />
== Team Contributions ==<br />
See the collection of videos on the [http://vimeo.com/album/2363361 Kitware vimeo album].<br />
<br />
== User Contributions ==<br />
See the [[Documentation/{{documentation/version}}/Training/UserContributions|User Contributions Page]] for more content.<br />
<br />
[http://www.youtube.com/results?search_query=3d+slicer&sm=3 YouTube videos about 3D Slicer]</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/4.8/Training&diff=56952Documentation/4.8/Training2017-10-24T18:24:33Z<p>Lauren: /* Slicer4 Diffusion Tensor Imaging Tutorial */</p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
=Introduction: Slicer {{documentation/version}} Tutorials=<br />
<br />
*This page contains "How to" tutorials with matched sample data sets. They demonstrate how to use the 3D Slicer environment (version {{documentation/version}} release) to accomplish certain tasks. <br />
*For tutorials for other versions of Slicer, please visit the [[Training| Slicer training portal]].<br />
*For "reference manual" style documentation, please visit the [[Documentation/{{documentation/version}}|Slicer {{documentation/version}} documentation page]]<br />
*For questions related to the Slicer4 Training Compendium, please send an e-mail to '''[http://www.na-mic.org/Wiki/index.php/User:SPujol Sonia Pujol, Ph.D., Director of Training of 3D Slicer.]'''<br />
<br />
<br />
* Some of these tutorials are based on older releases of 3D Slicer. The concepts are still useful but bear in mind that some interface elements and features will be different in updated versions.<br />
<br />
__TOC__<br />
<br />
=General Introduction=<br />
<br />
==Slicer Welcome Tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:SlicerWelcome-tutorial_Slicer4.5.pdf|SlicerWelcome tutorial]] is an introduction to Slicer based on the Welcome module.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want a general introduction to the software.<br />
*Modules: Welcome to Slicer, Sample Data<br />
*Based on: 3D Slicer version 4.6<br />
|align="right"|<br />
[[image:SlicerWelcome-image.png|250px|SlicerWelcome tutorial]]<br />
|}<br />
<br />
==Slicer4Minute Tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:Slicer4.5minute_SoniaPujol.pdf|Slicer4Minute tutorial]] is a brief introduction to the advanced 3D visualization capabilities of Slicer 4.5.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want to discover Slicer in 4 minutes.<br />
*Modules: Welcome to Slicer, Models<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:Slicer4minute.zip|Slicer4Minute dataset]] contains an MR scan of the brain and 3D reconstructions of the anatomy<br />
|align="right"|<br />
[[image:Slicer4minute-image.png|250px|right|Slicer4Minute tutorial]]<br />
|}<br />
<br />
==Slicer4 Data Loading and 3D Visualization ==<br />
{|width="100%"<br />
|<br />
*The [[Media:3DDataLoadingandVisualization_Slicer4.5_SoniaPujol.pdf | Data loading and 3D visualization]] course guides through the basics of loading and viewing volumes and 3D models in Slicer4 . <br />
*Author: Sonia Pujol, Ph.D.<br />
*Modules: Welcome to Slicer, Sample Data, Models.<br />
*Audience: End-users<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualizationData.zip | 3DVisualization dataset]] contain an MR scan and a series of 3D models of the brain.<br />
|align="right"|<br />
[[Image:Slicer4DataLoading_tutorial.png|right|250px|]]<br />
|}<br />
<br />
=Tutorials for software developers=<br />
<br />
== Slicer4 Programming Tutorial ==<br />
{|width="100%"<br />
|<br />
*The [https://www.dropbox.com/s/wrhrvvmplosiis1/Slicer4_ProgrammingTutorial_SPujol-SPieper_Nightly.pdf?dl=0# Slicer Programming tutorial] guides through the integration of a python module in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Steve Pieper, Ph.D.<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.7<br />
*The [https://www.dropbox.com/s/6yxu8qepmvywk0n/HelloPython_Nightly.zip?dl=0 HelloPython dataset] contains sample data set (MR scan of the brain) and complete Python module examples.<br />
|align="right"|<br />
[[Image:HelloPythonTutorial.png|right|250px|]]<br />
|}<br />
<br />
For additional Python scripts examples, please visit the [[Documentation/{{documentation/version}}/ScriptRepository|Script Repository page]]<br />
<br />
==Developing and contributing extensions for 3D Slicer==<br />
{|width="100%"<br />
|<br />
*The [http://goo.gl/IP4cdg Developing and contributing extensions for 3D Slicer tutorial ] is an introduction to the internals of 3D Slicer and the process of contributing a 3D Slicer extension.<br />
*Authors: Andrey Fedorov, Jean-Christophe Fillion-Robin, Steve Pieper<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.4<br />
|align="right"|<br />
[[Image:Contributing3DSlicerExtension.png|right|250px|]]<br />
|}<br />
<br />
=Specific functions=<br />
<br />
==Slicer4 Diffusion Tensor Imaging Tutorial ==<br />
{|width="100%"<br />
|<br />
*Please visit [http://dmri.slicer.org/docs/ dmri.slicer.org/docs] for the latest documentation of SlicerDMRI.<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/DiffusionMRIanalysis.pdf Diffusion Tensor Imaging Tutorial] course guides through the basics of loading Diffusion Weighted images in Slicer, estimating tensors and generating fiber tracts. <br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: End-users and developers<br />
*Modules: Data, Volumes, DWI to DTI Estimation, Diffusion Tensor Scalar Measurements, Editor, Markups,Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.6<br />
*The [[media:Dti tutorial data.zip|DTI dataset]] contains an MR Diffusion Weighted Imaging scan of the brain.<br />
|align="right"|<br />
[[Image:Slicer4DTI Tutorial.png|right|250px|]]<br />
|}<br />
<br />
==Slicer4 Neurosurgical Planning Tutorial==<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/WhiteMatterExplorationTutorial.pdf Neurosurgical Planning tutorial] course guides through the generation of fiber tracts in the vicinity of a tumor.<br />
*Author: Sonia Pujol, Ph.D., Ron Kikinis, M.D.<br />
*Audience: End-users and developers<br />
*Modules: Volumes, Editor, Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:WhiteMatterExplorationData.zip| White Matter Exploration datasets]] contains a Diffusion Weighted Imaging scan of brain tumor patient.<br />
|align="right"|<br />
[[Image:NeurosurgicalPlanningTutorial.png|right|250px|link=http://vimeo.com/67336069]]<br />
|}<br />
<br />
==Slicer4 3D Visualization of DICOM images for Radiology Applications==<br />
{|width="100%"<br />
|<br />
*The [[Media:3DSlicer_Dicom_RSNA2015_SoniaPujol.pdf |3D Visualization of DICOM images for Radiology Applications]] course guides through 3D data loading and visualization of DICOM images for Radiology Applications in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Kitt Shaffer, M.D., Ph.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 visualization capabilities.<br />
*Modules: DICOM, Volumes, Volume Rendering, Models.<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualization_DICOM_images_part1.zip | 3DVisualizationDICOM_part1]] and [[Media:3DVisualization_DICOM_images_part2.zip | 3DVisualizationDICOM_part2]] datasets contain a series of MR and CT scans, and 3D models of the brain, lung and liver.<br />
|align="right"|<br />
[[Image:Slicer4RSNA_2.png|right|250px|]]<br />
|}<br />
<br />
==Slicer4 Quantitative Imaging tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:QuantitativeImaging_Slicer4.5.pdf | Slicer4 Quantitative Imaging tutorial]] guides through the use for Slicer for quantifying small volumetric changes in slow-growing tumors, and for calculating Standardized Uptake Value (SUV) from PET/CT data.<br />
*Authors: Sonia Pujol, Ph.D., Katarzyna Macura, M.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 quantitative imaging capabilities.<br />
*Modules: Data, Volumes, Models, Change Tracker, PET Standard Uptake Value Computation<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:QuantitativeImaging.zip| Quantitative Imaging dataset]] contains a series of MR and PET/CT data.<br />
|align="right"|<br />
[[Image:Slicer4_QuantitativeImaging.png|right|250px|]]<br />
|}<br />
<br />
== Slicer4 IGT ==<br />
{|width="100%"<br />
|<br />
*[http://www.slicerigt.org/wp/user-tutorial/ Slicer IGT tutorials]<br />
*Authors: Tamas Ungi, M.D, Ph.D., Junichi Tokuda, Ph.D.<br />
*Audience: End-users interested in using Slicer for real-time navigated procedures. E.g. navigated needle insertions or other minimally invasive medical procedures.<br />
*Modules: SlicerIGT Extension<br />
*Based on: Slicer4.3.1-2014.09.14<br />
*Data: [https://onedrive.live.com/redir?resid=7230D4DEC6058018!2937&authkey=!AGQkSCZOwjVYXw8&ithint=folder%2cpptx Slicer-IGT datasets]<br />
|align="right"|<br />
[[Image:SlicetIGT.png|right|150px|]]<br />
|}<br />
<br />
== Slicer4 3D Printing ==<br />
<br />
{|width="100%"<br />
|<br />
* The video tutorial [https://youtu.be/Uht6Fwtr9hE Segmenting a CT for 3D Printing of a Lumbar Phantom] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.7.<br />
** Author: Hillary Lia<br />
** Audience: Users and developers interested in 3D printing<br />
* The [https://www.slicer.org/wiki/Documentation/4.6/Training#Segmentation_for_3D_printing Segmentation for 3D printing] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.6.<br />
** Author: Csaba Pinter, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
* This ''Slicer 4.3 [https://www.youtube.com/watch?v=MKLWzD0PiIc 3D printing tutorial]'' shows how to prepare 3D Slicer data for 3D printing using legacy Editor module.<br />
** Authors: Nabgha Farhat, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
|align="right"|[[Image:20170717_3DPrintingTutorialYoutube.PNG|280px]]<br />
|}<br />
<br />
== Slicer4 Image Registration ==<br />
<br />
{|width="100%"<br />
|<br />
*The [https://www.slicer.org/slicerWiki/index.php/File:RegistrationTutorial_3DSlicer4.5_spujol.pdf Registration tutorial] shows how to perform intra- and inter-subject registration within Slicer.<br />
* Authors: Sonia Pujol, Ph.D., Dominik Meier, Ph.D., Ron Kikinis, M.D.<br />
* Audience: Users and developers interested in image registration<br />
* Dataset: [[Media:RegistrationData.zip| 3D Slicer Registration Data]]<br />
|align="right"|[[File:registration_Slicer4.png|250px]]<br />
|}<br />
*Based on: 3D Slicer version 4.5<br />
See [[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|the Registration Library for worked out registration examples with data]].<br />
<br />
== Fast GrowCut ==<br />
<br />
{|width="100%"<br />
|<br />
* The [[media:FastGrowCutTutorial.pdf |Fast GrowCut tutorial]] shows how to perform a segmentation using the Fast GrowCut effect in Slicer.<br />
* Authors: Hillary Lia<br />
* Audience: Users interested in segmentation<br />
|align="right"|[[File:FastGrowCutLogo.png|200px]]<br />
|}<br />
<br />
==Slicer4 Radiation Therapy Tutorial ==<br />
** The [https://app.assembla.com/spaces/slicerrt/subversion/source/HEAD/trunk/SlicerRt/doc/tutorials/SlicerRT_TutorialIGRT_4.7.pdf?_format=raw SlicerRT tutorial] is an introduction to the Radiation Therapy functionalities of Slicer.<br />
** Author: Csaba Pinter, Andras Lasso, An Wang, Gregory C. Sharp, David Jaffray, Gabor Fichtinger. <br />
** Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip download] from MIDAS server<br />
**Based on Slicer 4.7<br />
<br />
== Other ==<br />
<br />
Additional (non-curated) videos-based demonstrations using 3D Slicer are accessible on [http://www.youtube.com/results?search_query=3d+slicer&sm=3 You Tube].<br />
<br />
= 3D Slicer Tutorial contests=<br />
<br />
==Winter 2017 Tutorial contest==<br />
<br />
===Segmentation for 3D printing===<br />
{|width="100%"<br />
|<br />
*The [https://www.assembla.com/spaces/slicerrt/documents/bmRQGEzzur54v-dmr6CpXy/download/bmRQGEzzur54v-dmr6CpXy Segmentation for 3D printing Tutorial] is an introduction to the new [[Documentation/{{documentation/version}}/Modules/SegmentEditor|Segment Editor]] module, demonstrated through the popular topic of 3D printing. <br />
*Author: Csaba Pinter (Queen's University, Canada)<br />
* [https://www.youtube.com/watch?v=Uht6Fwtr9hE Narrated video version on YouTube].<br />
*Dataset: [[:File:BasePiece.zip|Phantom base STL model]] Source: [http://perk-software.cs.queensu.ca/plus/doc/nightly/modelcatalog/ PerkLab].<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-Segmentation-for-3d-printing.png | 200px]]. <br />
|}<br />
<br />
===Slicer Pathology===<br />
{|width="100%"<br />
|<br />
*The [[Documentation/{{documentation/version}}/Extensions/SlicerPathology|Slicer Pathology Tutorial]] describes how to use the corresponding tools for automatic and semi-automatic pathology image segmentation.<br />
*Author: Erich Bremer (Stonybrook), Andriy Fedorov (Brigham and Women’s Hospital)<br />
*Dataset: Available directly with the Slicer Pathology Slicer extension.<br />
|align="right"|<br />
[[File:SlicerPathologyScreenShot8.png | 200px]]. <br />
|}<br />
<br />
===Simple Python Tool for Quality Control of DWI data===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/3/3a/SimpleDiffusionGradientInformationExtractorTutorial_Chauvin_Jan2017.pptx Simple Multi-shell Diffusion Gradients Information Extractor Tutorial] describes how to use a simple Python script for parsing multi-shell sensitizing gradients information from nifti file format (separated bvecs, bvals files).<br />
*Author: Laurent Chauvin (ETS Montreal)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SimpleDiffusionGradientInformationExtractorTutorial.png | 200px]]. <br />
|}<br />
<br />
===SPHARM-PDM===<br />
{|width="100%"<br />
|<br />
*The [https://www.nitrc.org/docman/view.php/308/1982/SPHARM-PDM_Tutorial_July2015.pdf SPHARM-PDM Tutorial] describes how to use SPHARM-PDM and ShapePopulationViewer Slicer extensions to respectively compute point-based models using a parametric boundary description for the computing of Shape Analysis and perform the quality control between the different models.<br />
*Author: Jonathan Perdomo (UNC), Beatriz Paniagua (Kitware Inc.)<br />
*Dataset: [https://www.nitrc.org/docman/view.php/308/1981/SPHARM_Tutorial_Data_July2015.zip Tutorial Data]<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SPHARM-PDM.png | 200px]]. <br />
|}<br />
<br />
===Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*The [https://www.na-mic.org/Wiki/images/a/ab/ROSIGTLTutorial_Tokuda_Jan2017.pptx Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink Tutorial] describes the software architecture of surgical robot systems and allows to acquire hands-on experience of software-hardware integration for medical robotics.<br />
*Author: Junichi Tokuda (Brigham and Women’s Hospital)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-Integration-ROS-3DSlicer-OpenIGTLink.png | 200px]]. <br />
|}<br />
<br />
===Fiber Bundle Volume Measurement===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/5/57/Fiber_Bundle_Volume_Measurement.pptx Fiber Bundle Volume Measurement Tutorial] aim is to calculate the volume of the fiber bundle that passes through the Corpus Callosum(CC). Following this tutorial, you’ll be able to (1) convert fiber bundles to label map and (2) calculate volume measurements from the fiber bundles.<br />
*Author: Shun Gong (Shanghai Changzheng Hospital, China)<br />
*Dataset: [http://www.na-mic.org/Wiki/images/4/4c/FiberVolume_data.zip Tutorial data]: The following data are provided: Baseline image, Down sampled whole brain tractography (conducted as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]] and down-sampled to about 10000 fibers using Tractography Display module), Corpus callosum label map (drawn as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]]).<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-FiberBundleVolumeMeasurements.png | 200px]]. <br />
|}<br />
<br />
==Winter 2016 Tutorial contest==<br />
<br />
===Subject Hierarchy===<br />
{|width="100%"<br />
|<br />
*The [http://wiki.na-mic.org/Wiki/images/2/27/SubjectHierarchy.TutorialContestWinter2016.pdf Subject Hierarchy] tutorial demonstrates the basic usage and potential of Slicer’s data manager module Subject Hierarchy using a two-timepoint radiotherapy phantom dataset.<br />
*Author: Csaba Pinter, Queen's University, Canada<br />
*Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip SlicerRT_WorldCongress_TutorialIGRT_Dataset] The tutorial dataset is a two-timepoint phantom dataset taken from a RANDO head&neck phantom. It contains two studies, the planning one is a DICOM study consisting of a CT grayscale image and radiotherapy data: contours, dose distribution, treatment beams, plan information. The second timepoint consists of a CT NRRD volume and a dose NRRD volume.<br />
|align="right"|<br />
[[File:SubjectHierarchyTutorial.png | 200px]]. <br />
|}<br />
<br />
===Fiber Bundle Selection and Scalar Measurements===<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/FiberBundleSelectionAndScalarMeasurement.pdf Fiber Bundle Selection and Scalar Measurements] tutorial guides through the use of the Diffusion Bundle Selection module and the Fiber Tract Scalar Measurement module for diffusion MRI tractography data analysis.<br />
*Author: Fan Zhang, University of Sydney Australia and Brigham and Women's Hospital<br />
*Dataset: [[media:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016.zip| Fiber Bundle Selection And Scalar Measurement Tutorial Dataset]]<br />
|align="right"|<br />
[[File:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016_Snapshot.png|200px]]<br />
|}<br />
<br />
===Plastimatch ===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/5/5c/Plastimatch_TutorialContestWinter2016.pdf Plastimatch tutorial] guides through registration and wrapping of DICOM and DICOM-RT data using the Plastimatch extension of 3D Slicer.<br />
*Author: Gregory Sharp, Massachusetts General Hospital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:Plastimatch_TutorialContestWinter2016.zip Plastimatch Tutorial Dataset]<br />
|align="right"|<br />
[[File:PlastimatchTutorial_Winter2016Contest.png|200px]]<br />
|}<br />
<br />
===UKF ===<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/UKFTractography.pdf UKF tutorial] guides through the use of the Unscented Kalman Filter (UKF) tractography module. <br />
*Author: Pegah Kahali, Brigham and Women's Hopital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:UKF-Tractography_TutorialContestWinter2016.zip UKF tutorial Dataset]<br />
|align="right"|<br />
[[File:UKF_Winter2016.png|200px]]<br />
|}<br />
<br />
==Summer 2014 Tutorial contest== <br />
<br />
===Cardiac Agatston Tutorial===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:TutorialContest_CardiacAgatstonScoring_2014.pdf Cardiac Agatston Scoring Tutorial]<br />
*Authors: Jessica Forbes, Hans Johnson, University of Iowa<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CardiacAgatstonMeasures_TutorialContestSummer2014.zip Cardiac Agatston Scoring Tutorial Dataset]<br />
|align="right"|<br />
[[File:CardiacAgatstonMeasuresModuleScreenshot.jpg| 250px]]<br />
|}<br />
<br />
===CMR Toolkit LA workflow===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflow_TutorialContestSummer2014.pdf CMR Toolkit LA Workflow Tutorial]<br />
*Authors: Salma Bengali, Josh Cates, University of Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflowData_TutorialContestSummer2014.zip CMRToolkitLAWorkflow Dataset]<br />
|align="right"|<br />
[[Image:Utah_SummerContest2014_tutorial.png|300px]]<br />
|}<br />
<br />
==Summer 2013 Tutorial contest==<br />
<br />
===Cardiac MRI Toolkit===<br />
{|width="100%"<br />
|<br />
*[[Media:Cardiac MRI Toolkit Tutorial Summer2013.pdf|Cardiac MRI Toolkit]]<br />
*Authors: Salma Bengali, Josh Cates, SCI, Utah<br />
*Dataset: [[Media:Cardiac_MRI_Toolkit_Tutorial_Data.zip|Cardiac MRI Toolkit Tutorial Dataset]]<br />
|align="right"|<br />
[[Image:CMRToolkit_Tutorial_Image.png|250px]]<br />
|}<br />
<br />
===HelloCLI===<br />
{|width="100%"<br />
|<br />
*[[Media:Hello_CLI_TutorialContestSummer2013.pdf|HelloCLI]]<br />
*Authors: Nadya Shusharina, Greg Sharp, MGH, Boston<br />
*Dataset: [[Media:Hello_CLI_TutorialContestSummer2013.zip|HelloCLI Dataset]]<br />
|align="right"|<br />
[[Image:Cli_icon.png|300px]]<br />
|}<br />
<br />
===SlicerRT===<br />
{|width="100%"<br />
|<br />
*[[Media:SlicerRT_TutorialContestSummer2013.pdf|SlicerRT Tutorial]]<br />
*Authors: Csaba Pinter, Andras Lasso (Queen's), Kevin Wang (PMH, Toronto)<br />
*Dataset: [[Media:CsabaPinter-SlicerRtTutorial_Namic2013June.zip|SlicerRT Dataset]] <br />
|align="right"|<br />
[[Image:667px-SlicerRT_0.10_IsocenterShiftingEvaluation.png|250px]]<br />
|}<br />
<br />
===DTIPrep===<br />
{|width="100%"<br />
|<br />
*[[Media:DTIPrep_TutorialContestSummer2013.pdf|DTIPrep]]<br />
*Authors: Dave Welch, SINAPSE, IOWA <br />
*Dataset: [[Media:DTIPrepData_TutorialContestSummer2013.zip|DTIPrep Dataset]]<br />
|align="right"|<br />
[[Image:DTIPrep-tutorial.png|250px]]<br />
|}<br />
<br />
== Summer 2012 Tutorial contest == <br />
<br />
===Automatic Left Atrial Scar Segmenter ===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/CARMA-LA-Scar_TutorialContestSummer2012 Automatic Left Atrial Scar Segmenter] <br />
*Authors: Greg Gardner, Josh Cates, SCI, Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CARMA-LA-Scar_TutorialContestSummer2012.zip CARMA-LA-Scar data]<br />
|align="right"|<br />
[[Image:Carma afib auto scar.png|250px]]<br />
|}<br />
<br />
===Qualitative and quantitative comparison of two RT dose distributions===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:PlastimatchDose_TutorialContestSummer2012.pdf Qualitative and quantitative comparison of two RT dose distributions]<br />
*Authors: James Shackleford, Nadya Shusharina, Greg Sharp, MGH<br />
|align="right"|<br />
[[Image:PlastimatchDose.png|250px]]<br />
|}<br />
<br />
===Dose accumulation for adaptive radiation therapy===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:DoseAccumulationforAdaptiveRadiationTherapy_TutorialContestSummer2012.pdf Dose accumulation for adaptive radiation therapy]<br />
*Authors: Kevin Wang, Csaba Pinter, Andras Lasso, PMH, Queen's<br />
|align="right"|<br />
[[Image:AdaptiveradiationTherapy.png|250px]]<br />
|}<br />
<br />
===WebGL Export===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:WebGLExport_TutorialContestSummer2012.pdf WebdGLExport]<br />
*Authors: Nicolas Rannou, Daniel Haehn, Children's Hospital<br />
|align="right"|<br />
[[Image:WebGLExport.png|250px]]<br />
|}<br />
<br />
===OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*[http://wiki.slicer.org/slicerWiki/images/f/f1/OpenIGTLinkTutorial_Slicer4.1.0_JunichiTokuda_Apr2012.pdf OpenIGTLink]<br />
*Authors: Junichi Tokuda, BWH<br />
|align="right"|<br />
[[Image:OpenIGTLink.png|250px]]<br />
|}<br />
<br />
=Additional resources =<br />
{|width="100%"<br />
|<br />
* This ''Slicer 4.1 [http://vimeo.com/41096643 webinar]'' presents the new features and improvements of the release, and a brief overview of work for the next release.<br />
* Authors: Steve Pieper Ph.D.<br />
* Audience: First time users and developers interested in Slicer 4.1 new features.<br />
* Length: 0h20m<br />
|align="right"|[[Image:Webinar-Slicer-4.1.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*This ''Intro to Slicer 4.0 [http://vimeo.com/37671358 webinar]'' provides an introduction to 3DSlicer, and demonstrates core functionalities such as loading, visualizing and saving data. Basic processing tools, including manual registration, manual segmentation and tractography tools are also highlighted. This webinar is a general overview. For in depth information see the modules above and the documentation pages.<br />
*Authors: Julien Finet, M.S., Steve Pieper, Ph.D., Jean-Christophe Fillion-Robin, M.S. <br />
*Audience: First time users interested in a broad overview of Slicer’s features and tools.<br />
*Length: 1h20m<br />
|align="right"|[[Image:Webinar.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*The ''[[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|Slicer Registration Case Library]]'' provides many real-life example cases of using the Slicer registration tools. They include the dataset and step-by-step instructions to follow and try yourself. <br />
:Author: Dominik Meier, Ph.D.<br />
:Audience: users interested learning/applying Slicer image registration technology<br />
|align="right"|[[Image:RegLib_table.png|250px|link=http://wiki.slicer.org/slicerWiki/index.php/Documentation/{{documentation/version}}/Registration/RegistrationLibrary]]<br />
|}<br />
<br />
= External Resources =<br />
<br />
== Murat Maga's blog posts about using 3D Slicer for biology ==<br />
<br />
* [https://blogs.uw.edu/maga/2017/04/11/getting-started-with-3d-slicer-as-a-biologist/ Slicer for Biologists]<br />
* [https://blogs.uw.edu/maga/2017/04/11/a-worked-example-getting-and-visualizing-data-from-digimorph/ Loading data from DigiMorph]<br />
* [https://blogs.uw.edu/maga/2017/04/11/morphosource-data-and-dealing-with-dicom-series-in-slicer/ Fixing problem DICOM]<br />
* [https://blogs.uw.edu/maga/2017/04/12/scissors-tool-is-awesome/ Scissors tool is awesom]<br />
<br />
== Using the (legacy) Editor ==<br />
<br />
This set of tutorials about the use of slicer in paleontology is very well written and provides step-by-step instructions. Even though it covers slicer version 3.4, many of the concepts and techniques have applicability to the new version and to any 3D imaging field:<br />
<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial.html Open Source Paleontologist: 3D Slicer: The Tutorial]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-ii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part II]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part III]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iv.html Open Source Paleontologist: 3D Slicer: The Tutorial Part IV]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-v.html Open Source Paleontologist: 3D Slicer: The Tutorial Part V]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-vi.html Open Source Paleontologist: 3D Slicer: The Tutorial Part VI]<br />
<br />
== Team Contributions ==<br />
See the collection of videos on the [http://vimeo.com/album/2363361 Kitware vimeo album].<br />
<br />
== User Contributions ==<br />
See the [[Documentation/{{documentation/version}}/Training/UserContributions|User Contributions Page]] for more content.<br />
<br />
[http://www.youtube.com/results?search_query=3d+slicer&sm=3 YouTube videos about 3D Slicer]</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Training&diff=56949Documentation/Nightly/Training2017-10-24T18:24:02Z<p>Lauren: /* Slicer4 Neurosurgical Planning Tutorial */</p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
=Introduction: Slicer {{documentation/version}} Tutorials=<br />
<br />
*This page contains "How to" tutorials with matched sample data sets. They demonstrate how to use the 3D Slicer environment (version {{documentation/version}} release) to accomplish certain tasks. <br />
*For tutorials for other versions of Slicer, please visit the [[Training| Slicer training portal]].<br />
*For "reference manual" style documentation, please visit the [[Documentation/{{documentation/version}}|Slicer {{documentation/version}} documentation page]]<br />
*For questions related to the Slicer4 Training Compendium, please send an e-mail to '''[http://www.na-mic.org/Wiki/index.php/User:SPujol Sonia Pujol, Ph.D., Director of Training of 3D Slicer.]'''<br />
<br />
<br />
* Some of these tutorials are based on older releases of 3D Slicer. The concepts are still useful but bear in mind that some interface elements and features will be different in updated versions.<br />
<br />
__TOC__<br />
<br />
=General Introduction=<br />
<br />
==Slicer Welcome Tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:SlicerWelcome-tutorial_Slicer4.5.pdf|SlicerWelcome tutorial]] is an introduction to Slicer based on the Welcome module.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want a general introduction to the software.<br />
*Modules: Welcome to Slicer, Sample Data<br />
*Based on: 3D Slicer version 4.6<br />
|align="right"|<br />
[[image:SlicerWelcome-image.png|250px|SlicerWelcome tutorial]]<br />
|}<br />
<br />
==Slicer4Minute Tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:Slicer4.5minute_SoniaPujol.pdf|Slicer4Minute tutorial]] is a brief introduction to the advanced 3D visualization capabilities of Slicer 4.5.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want to discover Slicer in 4 minutes.<br />
*Modules: Welcome to Slicer, Models<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:Slicer4minute.zip|Slicer4Minute dataset]] contains an MR scan of the brain and 3D reconstructions of the anatomy<br />
|align="right"|<br />
[[image:Slicer4minute-image.png|250px|right|Slicer4Minute tutorial]]<br />
|}<br />
<br />
==Slicer4 Data Loading and 3D Visualization ==<br />
{|width="100%"<br />
|<br />
*The [[Media:3DDataLoadingandVisualization_Slicer4.5_SoniaPujol.pdf | Data loading and 3D visualization]] course guides through the basics of loading and viewing volumes and 3D models in Slicer4 . <br />
*Author: Sonia Pujol, Ph.D.<br />
*Modules: Welcome to Slicer, Sample Data, Models.<br />
*Audience: End-users<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualizationData.zip | 3DVisualization dataset]] contain an MR scan and a series of 3D models of the brain.<br />
|align="right"|<br />
[[Image:Slicer4DataLoading_tutorial.png|right|250px|]]<br />
|}<br />
<br />
=Tutorials for software developers=<br />
<br />
== Slicer4 Programming Tutorial ==<br />
{|width="100%"<br />
|<br />
*The [https://www.dropbox.com/s/wrhrvvmplosiis1/Slicer4_ProgrammingTutorial_SPujol-SPieper_Nightly.pdf?dl=0# Slicer Programming tutorial] guides through the integration of a python module in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Steve Pieper, Ph.D.<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.7<br />
*The [https://www.dropbox.com/s/6yxu8qepmvywk0n/HelloPython_Nightly.zip?dl=0 HelloPython dataset] contains sample data set (MR scan of the brain) and complete Python module examples.<br />
|align="right"|<br />
[[Image:HelloPythonTutorial.png|right|250px|]]<br />
|}<br />
<br />
For additional Python scripts examples, please visit the [[Documentation/{{documentation/version}}/ScriptRepository|Script Repository page]]<br />
<br />
==Developing and contributing extensions for 3D Slicer==<br />
{|width="100%"<br />
|<br />
*The [http://goo.gl/IP4cdg Developing and contributing extensions for 3D Slicer tutorial ] is an introduction to the internals of 3D Slicer and the process of contributing a 3D Slicer extension.<br />
*Authors: Andrey Fedorov, Jean-Christophe Fillion-Robin, Steve Pieper<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.4<br />
|align="right"|<br />
[[Image:Contributing3DSlicerExtension.png|right|250px|]]<br />
|}<br />
<br />
=Specific functions=<br />
<br />
==Slicer4 Diffusion Tensor Imaging Tutorial ==<br />
{|width="100%"<br />
|<br />
*Please visit [http://dmri.slicer.org/docs/ dmri.slicer.org/docs] for the latest documentation of SlicerDMRI.<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/DiffusionMRIanalysis.pdf Diffusion Tensor Imaging Tutorial] course guides through the basics of loading Diffusion Weighted images in Slicer, estimating tensors and generating fiber tracts. <br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: End-users and developers<br />
*Modules: Data, Volumes, DWI to DTI Estimation, Diffusion Tensor Scalar Measurements, Editor, Markups,Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:Dti tutorial data.zip|DTI dataset]] contains an MR Diffusion Weighted Imaging scan of the brain.<br />
|align="right"|<br />
[[Image:Slicer4DTI Tutorial.png|right|250px|]]<br />
|}<br />
<br />
==Slicer4 Neurosurgical Planning Tutorial==<br />
{|width="100%"<br />
|<br />
*Please visit [http://dmri.slicer.org/docs/ dmri.slicer.org/docs] for the latest documentation of SlicerDMRI.<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/WhiteMatterExplorationTutorial.pdf Neurosurgical Planning tutorial] course guides through the generation of fiber tracts in the vicinity of a tumor.<br />
*Author: Sonia Pujol, Ph.D., Ron Kikinis, M.D.<br />
*Audience: End-users and developers<br />
*Modules: Volumes, Editor, Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:WhiteMatterExplorationData.zip| White Matter Exploration datasets]] contains a Diffusion Weighted Imaging scan of brain tumor patient.<br />
|align="right"|<br />
[[Image:NeurosurgicalPlanningTutorial.png|right|250px|link=http://vimeo.com/67336069]]<br />
|}<br />
<br />
==Slicer4 3D Visualization of DICOM images for Radiology Applications==<br />
{|width="100%"<br />
|<br />
*The [[Media:3DSlicer_Dicom_RSNA2015_SoniaPujol.pdf |3D Visualization of DICOM images for Radiology Applications]] course guides through 3D data loading and visualization of DICOM images for Radiology Applications in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Kitt Shaffer, M.D., Ph.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 visualization capabilities.<br />
*Modules: DICOM, Volumes, Volume Rendering, Models.<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualization_DICOM_images_part1.zip | 3DVisualizationDICOM_part1]] and [[Media:3DVisualization_DICOM_images_part2.zip | 3DVisualizationDICOM_part2]] datasets contain a series of MR and CT scans, and 3D models of the brain, lung and liver.<br />
|align="right"|<br />
[[Image:Slicer4RSNA_2.png|right|250px|]]<br />
|}<br />
<br />
==Slicer4 Quantitative Imaging tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:QuantitativeImaging_Slicer4.5.pdf | Slicer4 Quantitative Imaging tutorial]] guides through the use for Slicer for quantifying small volumetric changes in slow-growing tumors, and for calculating Standardized Uptake Value (SUV) from PET/CT data.<br />
*Authors: Sonia Pujol, Ph.D., Katarzyna Macura, M.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 quantitative imaging capabilities.<br />
*Modules: Data, Volumes, Models, Change Tracker, PET Standard Uptake Value Computation<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:QuantitativeImaging.zip| Quantitative Imaging dataset]] contains a series of MR and PET/CT data.<br />
|align="right"|<br />
[[Image:Slicer4_QuantitativeImaging.png|right|250px|]]<br />
|}<br />
<br />
== Slicer4 IGT ==<br />
{|width="100%"<br />
|<br />
*[http://www.slicerigt.org/wp/user-tutorial/ Slicer IGT tutorials]<br />
*Authors: Tamas Ungi, M.D, Ph.D., Junichi Tokuda, Ph.D.<br />
*Audience: End-users interested in using Slicer for real-time navigated procedures. E.g. navigated needle insertions or other minimally invasive medical procedures.<br />
*Modules: SlicerIGT Extension<br />
*Based on: Slicer4.3.1-2014.09.14<br />
*Data: [https://onedrive.live.com/redir?resid=7230D4DEC6058018!2937&authkey=!AGQkSCZOwjVYXw8&ithint=folder%2cpptx Slicer-IGT datasets]<br />
|align="right"|<br />
[[Image:SlicetIGT.png|right|150px|]]<br />
|}<br />
<br />
== Slicer4 3D Printing ==<br />
<br />
{|width="100%"<br />
|<br />
* The video tutorial [https://youtu.be/Uht6Fwtr9hE Segmenting a CT for 3D Printing of a Lumbar Phantom] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.7.<br />
** Author: Hillary Lia<br />
** Audience: Users and developers interested in 3D printing<br />
* The [https://www.slicer.org/wiki/Documentation/4.6/Training#Segmentation_for_3D_printing Segmentation for 3D printing] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.6.<br />
** Author: Csaba Pinter, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
* This ''Slicer 4.3 [https://www.youtube.com/watch?v=MKLWzD0PiIc 3D printing tutorial]'' shows how to prepare 3D Slicer data for 3D printing using legacy Editor module.<br />
** Authors: Nabgha Farhat, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
|align="right"|[[Image:20170717_3DPrintingTutorialYoutube.PNG|280px]]<br />
|}<br />
<br />
== Slicer4 Image Registration ==<br />
<br />
{|width="100%"<br />
|<br />
*The [https://www.slicer.org/slicerWiki/index.php/File:RegistrationTutorial_3DSlicer4.5_spujol.pdf Registration tutorial] shows how to perform intra- and inter-subject registration within Slicer.<br />
* Authors: Sonia Pujol, Ph.D., Dominik Meier, Ph.D., Ron Kikinis, M.D.<br />
* Audience: Users and developers interested in image registration<br />
* Dataset: [[Media:RegistrationData.zip| 3D Slicer Registration Data]]<br />
|align="right"|[[File:registration_Slicer4.png|250px]]<br />
|}<br />
*Based on: 3D Slicer version 4.5<br />
See [[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|the Registration Library for worked out registration examples with data]].<br />
<br />
== Fast GrowCut ==<br />
<br />
{|width="100%"<br />
|<br />
* The [[media:FastGrowCutTutorial.pdf |Fast GrowCut tutorial]] shows how to perform a segmentation using the Fast GrowCut effect in Slicer.<br />
* Authors: Hillary Lia<br />
* Audience: Users interested in segmentation<br />
|align="right"|[[File:FastGrowCutLogo.png|200px]]<br />
|}<br />
<br />
==Slicer4 Radiation Therapy Tutorial ==<br />
** The [https://app.assembla.com/spaces/slicerrt/subversion/source/HEAD/trunk/SlicerRt/doc/tutorials/SlicerRT_TutorialIGRT_4.7.pdf?_format=raw SlicerRT tutorial] is an introduction to the Radiation Therapy functionalities of Slicer.<br />
** Author: Csaba Pinter, Andras Lasso, An Wang, Gregory C. Sharp, David Jaffray, Gabor Fichtinger. <br />
** Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip download] from MIDAS server<br />
**Based on Slicer 4.7<br />
<br />
== Other ==<br />
<br />
Additional (non-curated) videos-based demonstrations using 3D Slicer are accessible on [http://www.youtube.com/results?search_query=3d+slicer&sm=3 You Tube].<br />
<br />
= 3D Slicer Tutorial contests=<br />
<br />
==Winter 2017 Tutorial contest==<br />
<br />
===Segmentation for 3D printing===<br />
{|width="100%"<br />
|<br />
*The [https://www.assembla.com/spaces/slicerrt/documents/bmRQGEzzur54v-dmr6CpXy/download/bmRQGEzzur54v-dmr6CpXy Segmentation for 3D printing Tutorial] is an introduction to the new [[Documentation/{{documentation/version}}/Modules/SegmentEditor|Segment Editor]] module, demonstrated through the popular topic of 3D printing. <br />
*Author: Csaba Pinter (Queen's University, Canada)<br />
* [https://www.youtube.com/watch?v=Uht6Fwtr9hE Narrated video version on YouTube].<br />
*Dataset: [[:File:BasePiece.zip|Phantom base STL model]] Source: [http://perk-software.cs.queensu.ca/plus/doc/nightly/modelcatalog/ PerkLab].<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-Segmentation-for-3d-printing.png | 200px]]. <br />
|}<br />
<br />
===Slicer Pathology===<br />
{|width="100%"<br />
|<br />
*The [[Documentation/{{documentation/version}}/Extensions/SlicerPathology|Slicer Pathology Tutorial]] describes how to use the corresponding tools for automatic and semi-automatic pathology image segmentation.<br />
*Author: Erich Bremer (Stonybrook), Andriy Fedorov (Brigham and Women’s Hospital)<br />
*Dataset: Available directly with the Slicer Pathology Slicer extension.<br />
|align="right"|<br />
[[File:SlicerPathologyScreenShot8.png | 200px]]. <br />
|}<br />
<br />
===Simple Python Tool for Quality Control of DWI data===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/3/3a/SimpleDiffusionGradientInformationExtractorTutorial_Chauvin_Jan2017.pptx Simple Multi-shell Diffusion Gradients Information Extractor Tutorial] describes how to use a simple Python script for parsing multi-shell sensitizing gradients information from nifti file format (separated bvecs, bvals files).<br />
*Author: Laurent Chauvin (ETS Montreal)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SimpleDiffusionGradientInformationExtractorTutorial.png | 200px]]. <br />
|}<br />
<br />
===SPHARM-PDM===<br />
{|width="100%"<br />
|<br />
*The [https://www.nitrc.org/docman/view.php/308/1982/SPHARM-PDM_Tutorial_July2015.pdf SPHARM-PDM Tutorial] describes how to use SPHARM-PDM and ShapePopulationViewer Slicer extensions to respectively compute point-based models using a parametric boundary description for the computing of Shape Analysis and perform the quality control between the different models.<br />
*Author: Jonathan Perdomo (UNC), Beatriz Paniagua (Kitware Inc.)<br />
*Dataset: [https://www.nitrc.org/docman/view.php/308/1981/SPHARM_Tutorial_Data_July2015.zip Tutorial Data]<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SPHARM-PDM.png | 200px]]. <br />
|}<br />
<br />
===Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*The [https://www.na-mic.org/Wiki/images/a/ab/ROSIGTLTutorial_Tokuda_Jan2017.pptx Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink Tutorial] describes the software architecture of surgical robot systems and allows to acquire hands-on experience of software-hardware integration for medical robotics.<br />
*Author: Junichi Tokuda (Brigham and Women’s Hospital)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-Integration-ROS-3DSlicer-OpenIGTLink.png | 200px]]. <br />
|}<br />
<br />
===Fiber Bundle Volume Measurement===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/5/57/Fiber_Bundle_Volume_Measurement.pptx Fiber Bundle Volume Measurement Tutorial] aim is to calculate the volume of the fiber bundle that passes through the Corpus Callosum(CC). Following this tutorial, you’ll be able to (1) convert fiber bundles to label map and (2) calculate volume measurements from the fiber bundles.<br />
*Author: Shun Gong (Shanghai Changzheng Hospital, China)<br />
*Dataset: [http://www.na-mic.org/Wiki/images/4/4c/FiberVolume_data.zip Tutorial data]: The following data are provided: Baseline image, Down sampled whole brain tractography (conducted as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]] and down-sampled to about 10000 fibers using Tractography Display module), Corpus callosum label map (drawn as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]]).<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-FiberBundleVolumeMeasurements.png | 200px]]. <br />
|}<br />
<br />
==Winter 2016 Tutorial contest==<br />
<br />
===Subject Hierarchy===<br />
{|width="100%"<br />
|<br />
*The [http://wiki.na-mic.org/Wiki/images/2/27/SubjectHierarchy.TutorialContestWinter2016.pdf Subject Hierarchy] tutorial demonstrates the basic usage and potential of Slicer’s data manager module Subject Hierarchy using a two-timepoint radiotherapy phantom dataset.<br />
*Author: Csaba Pinter, Queen's University, Canada<br />
*Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip SlicerRT_WorldCongress_TutorialIGRT_Dataset] The tutorial dataset is a two-timepoint phantom dataset taken from a RANDO head&neck phantom. It contains two studies, the planning one is a DICOM study consisting of a CT grayscale image and radiotherapy data: contours, dose distribution, treatment beams, plan information. The second timepoint consists of a CT NRRD volume and a dose NRRD volume.<br />
|align="right"|<br />
[[File:SubjectHierarchyTutorial.png | 200px]]. <br />
|}<br />
<br />
===Fiber Bundle Selection and Scalar Measurements===<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/FiberBundleSelectionAndScalarMeasurement.pdf Fiber Bundle Selection and Scalar Measurements] tutorial guides through the use of the Diffusion Bundle Selection module and the Fiber Tract Scalar Measurement module for diffusion MRI tractography data analysis.<br />
*Author: Fan Zhang, University of Sydney Australia and Brigham and Women's Hospital<br />
*Dataset: [[media:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016.zip| Fiber Bundle Selection And Scalar Measurement Tutorial Dataset]]<br />
|align="right"|<br />
[[File:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016_Snapshot.png|200px]]<br />
|}<br />
<br />
===Plastimatch ===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/5/5c/Plastimatch_TutorialContestWinter2016.pdf Plastimatch tutorial] guides through registration and wrapping of DICOM and DICOM-RT data using the Plastimatch extension of 3D Slicer.<br />
*Author: Gregory Sharp, Massachusetts General Hospital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:Plastimatch_TutorialContestWinter2016.zip Plastimatch Tutorial Dataset]<br />
|align="right"|<br />
[[File:PlastimatchTutorial_Winter2016Contest.png|200px]]<br />
|}<br />
<br />
===UKF ===<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/UKFTractography.pdf UKF tutorial] guides through the use of the Unscented Kalman Filter (UKF) tractography module. <br />
*Author: Pegah Kahali, Brigham and Women's Hopital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:UKF-Tractography_TutorialContestWinter2016.zip UKF tutorial Dataset]<br />
|align="right"|<br />
[[File:UKF_Winter2016.png|200px]]<br />
|}<br />
<br />
==Summer 2014 Tutorial contest== <br />
<br />
===Cardiac Agatston Tutorial===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:TutorialContest_CardiacAgatstonScoring_2014.pdf Cardiac Agatston Scoring Tutorial]<br />
*Authors: Jessica Forbes, Hans Johnson, University of Iowa<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CardiacAgatstonMeasures_TutorialContestSummer2014.zip Cardiac Agatston Scoring Tutorial Dataset]<br />
|align="right"|<br />
[[File:CardiacAgatstonMeasuresModuleScreenshot.jpg| 250px]]<br />
|}<br />
<br />
===CMR Toolkit LA workflow===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflow_TutorialContestSummer2014.pdf CMR Toolkit LA Workflow Tutorial]<br />
*Authors: Salma Bengali, Josh Cates, University of Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflowData_TutorialContestSummer2014.zip CMRToolkitLAWorkflow Dataset]<br />
|align="right"|<br />
[[Image:Utah_SummerContest2014_tutorial.png|300px]]<br />
|}<br />
<br />
==Summer 2013 Tutorial contest==<br />
<br />
===Cardiac MRI Toolkit===<br />
{|width="100%"<br />
|<br />
*[[Media:Cardiac MRI Toolkit Tutorial Summer2013.pdf|Cardiac MRI Toolkit]]<br />
*Authors: Salma Bengali, Josh Cates, SCI, Utah<br />
*Dataset: [[Media:Cardiac_MRI_Toolkit_Tutorial_Data.zip|Cardiac MRI Toolkit Tutorial Dataset]]<br />
|align="right"|<br />
[[Image:CMRToolkit_Tutorial_Image.png|250px]]<br />
|}<br />
<br />
===HelloCLI===<br />
{|width="100%"<br />
|<br />
*[[Media:Hello_CLI_TutorialContestSummer2013.pdf|HelloCLI]]<br />
*Authors: Nadya Shusharina, Greg Sharp, MGH, Boston<br />
*Dataset: [[Media:Hello_CLI_TutorialContestSummer2013.zip|HelloCLI Dataset]]<br />
|align="right"|<br />
[[Image:Cli_icon.png|300px]]<br />
|}<br />
<br />
===SlicerRT===<br />
{|width="100%"<br />
|<br />
*[[Media:SlicerRT_TutorialContestSummer2013.pdf|SlicerRT Tutorial]]<br />
*Authors: Csaba Pinter, Andras Lasso (Queen's), Kevin Wang (PMH, Toronto)<br />
*Dataset: [[Media:CsabaPinter-SlicerRtTutorial_Namic2013June.zip|SlicerRT Dataset]] <br />
|align="right"|<br />
[[Image:667px-SlicerRT_0.10_IsocenterShiftingEvaluation.png|250px]]<br />
|}<br />
<br />
===DTIPrep===<br />
{|width="100%"<br />
|<br />
*[[Media:DTIPrep_TutorialContestSummer2013.pdf|DTIPrep]]<br />
*Authors: Dave Welch, SINAPSE, IOWA <br />
*Dataset: [[Media:DTIPrepData_TutorialContestSummer2013.zip|DTIPrep Dataset]]<br />
|align="right"|<br />
[[Image:DTIPrep-tutorial.png|250px]]<br />
|}<br />
<br />
== Summer 2012 Tutorial contest == <br />
<br />
===Automatic Left Atrial Scar Segmenter ===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/CARMA-LA-Scar_TutorialContestSummer2012 Automatic Left Atrial Scar Segmenter] <br />
*Authors: Greg Gardner, Josh Cates, SCI, Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CARMA-LA-Scar_TutorialContestSummer2012.zip CARMA-LA-Scar data]<br />
|align="right"|<br />
[[Image:Carma afib auto scar.png|250px]]<br />
|}<br />
<br />
===Qualitative and quantitative comparison of two RT dose distributions===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:PlastimatchDose_TutorialContestSummer2012.pdf Qualitative and quantitative comparison of two RT dose distributions]<br />
*Authors: James Shackleford, Nadya Shusharina, Greg Sharp, MGH<br />
|align="right"|<br />
[[Image:PlastimatchDose.png|250px]]<br />
|}<br />
<br />
===Dose accumulation for adaptive radiation therapy===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:DoseAccumulationforAdaptiveRadiationTherapy_TutorialContestSummer2012.pdf Dose accumulation for adaptive radiation therapy]<br />
*Authors: Kevin Wang, Csaba Pinter, Andras Lasso, PMH, Queen's<br />
|align="right"|<br />
[[Image:AdaptiveradiationTherapy.png|250px]]<br />
|}<br />
<br />
===WebGL Export===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:WebGLExport_TutorialContestSummer2012.pdf WebdGLExport]<br />
*Authors: Nicolas Rannou, Daniel Haehn, Children's Hospital<br />
|align="right"|<br />
[[Image:WebGLExport.png|250px]]<br />
|}<br />
<br />
===OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*[http://wiki.slicer.org/slicerWiki/images/f/f1/OpenIGTLinkTutorial_Slicer4.1.0_JunichiTokuda_Apr2012.pdf OpenIGTLink]<br />
*Authors: Junichi Tokuda, BWH<br />
|align="right"|<br />
[[Image:OpenIGTLink.png|250px]]<br />
|}<br />
<br />
=Additional resources =<br />
{|width="100%"<br />
|<br />
* This ''Slicer 4.1 [http://vimeo.com/41096643 webinar]'' presents the new features and improvements of the release, and a brief overview of work for the next release.<br />
* Authors: Steve Pieper Ph.D.<br />
* Audience: First time users and developers interested in Slicer 4.1 new features.<br />
* Length: 0h20m<br />
|align="right"|[[Image:Webinar-Slicer-4.1.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*This ''Intro to Slicer 4.0 [http://vimeo.com/37671358 webinar]'' provides an introduction to 3DSlicer, and demonstrates core functionalities such as loading, visualizing and saving data. Basic processing tools, including manual registration, manual segmentation and tractography tools are also highlighted. This webinar is a general overview. For in depth information see the modules above and the documentation pages.<br />
*Authors: Julien Finet, M.S., Steve Pieper, Ph.D., Jean-Christophe Fillion-Robin, M.S. <br />
*Audience: First time users interested in a broad overview of Slicer’s features and tools.<br />
*Length: 1h20m<br />
|align="right"|[[Image:Webinar.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*The ''[[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|Slicer Registration Case Library]]'' provides many real-life example cases of using the Slicer registration tools. They include the dataset and step-by-step instructions to follow and try yourself. <br />
:Author: Dominik Meier, Ph.D.<br />
:Audience: users interested learning/applying Slicer image registration technology<br />
|align="right"|[[Image:RegLib_table.png|250px|link=http://wiki.slicer.org/slicerWiki/index.php/Documentation/{{documentation/version}}/Registration/RegistrationLibrary]]<br />
|}<br />
<br />
= External Resources =<br />
<br />
== Murat Maga's blog posts about using 3D Slicer for biology ==<br />
<br />
* [https://blogs.uw.edu/maga/2017/04/11/getting-started-with-3d-slicer-as-a-biologist/ Slicer for Biologists]<br />
* [https://blogs.uw.edu/maga/2017/04/11/a-worked-example-getting-and-visualizing-data-from-digimorph/ Loading data from DigiMorph]<br />
* [https://blogs.uw.edu/maga/2017/04/11/morphosource-data-and-dealing-with-dicom-series-in-slicer/ Fixing problem DICOM]<br />
* [https://blogs.uw.edu/maga/2017/04/12/scissors-tool-is-awesome/ Scissors tool is awesom]<br />
<br />
== Using the (legacy) Editor ==<br />
<br />
This set of tutorials about the use of slicer in paleontology is very well written and provides step-by-step instructions. Even though it covers slicer version 3.4, many of the concepts and techniques have applicability to the new version and to any 3D imaging field:<br />
<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial.html Open Source Paleontologist: 3D Slicer: The Tutorial]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-ii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part II]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part III]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iv.html Open Source Paleontologist: 3D Slicer: The Tutorial Part IV]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-v.html Open Source Paleontologist: 3D Slicer: The Tutorial Part V]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-vi.html Open Source Paleontologist: 3D Slicer: The Tutorial Part VI]<br />
<br />
== Team Contributions ==<br />
See the collection of videos on the [http://vimeo.com/album/2363361 Kitware vimeo album].<br />
<br />
== User Contributions ==<br />
See the [[Documentation/{{documentation/version}}/Training/UserContributions|User Contributions Page]] for more content.<br />
<br />
[http://www.youtube.com/results?search_query=3d+slicer&sm=3 YouTube videos about 3D Slicer]</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Training&diff=56946Documentation/Nightly/Training2017-10-24T18:23:51Z<p>Lauren: /* Slicer4 Diffusion Tensor Imaging Tutorial */</p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
=Introduction: Slicer {{documentation/version}} Tutorials=<br />
<br />
*This page contains "How to" tutorials with matched sample data sets. They demonstrate how to use the 3D Slicer environment (version {{documentation/version}} release) to accomplish certain tasks. <br />
*For tutorials for other versions of Slicer, please visit the [[Training| Slicer training portal]].<br />
*For "reference manual" style documentation, please visit the [[Documentation/{{documentation/version}}|Slicer {{documentation/version}} documentation page]]<br />
*For questions related to the Slicer4 Training Compendium, please send an e-mail to '''[http://www.na-mic.org/Wiki/index.php/User:SPujol Sonia Pujol, Ph.D., Director of Training of 3D Slicer.]'''<br />
<br />
<br />
* Some of these tutorials are based on older releases of 3D Slicer. The concepts are still useful but bear in mind that some interface elements and features will be different in updated versions.<br />
<br />
__TOC__<br />
<br />
=General Introduction=<br />
<br />
==Slicer Welcome Tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:SlicerWelcome-tutorial_Slicer4.5.pdf|SlicerWelcome tutorial]] is an introduction to Slicer based on the Welcome module.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want a general introduction to the software.<br />
*Modules: Welcome to Slicer, Sample Data<br />
*Based on: 3D Slicer version 4.6<br />
|align="right"|<br />
[[image:SlicerWelcome-image.png|250px|SlicerWelcome tutorial]]<br />
|}<br />
<br />
==Slicer4Minute Tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:Slicer4.5minute_SoniaPujol.pdf|Slicer4Minute tutorial]] is a brief introduction to the advanced 3D visualization capabilities of Slicer 4.5.<br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: First time users who want to discover Slicer in 4 minutes.<br />
*Modules: Welcome to Slicer, Models<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:Slicer4minute.zip|Slicer4Minute dataset]] contains an MR scan of the brain and 3D reconstructions of the anatomy<br />
|align="right"|<br />
[[image:Slicer4minute-image.png|250px|right|Slicer4Minute tutorial]]<br />
|}<br />
<br />
==Slicer4 Data Loading and 3D Visualization ==<br />
{|width="100%"<br />
|<br />
*The [[Media:3DDataLoadingandVisualization_Slicer4.5_SoniaPujol.pdf | Data loading and 3D visualization]] course guides through the basics of loading and viewing volumes and 3D models in Slicer4 . <br />
*Author: Sonia Pujol, Ph.D.<br />
*Modules: Welcome to Slicer, Sample Data, Models.<br />
*Audience: End-users<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualizationData.zip | 3DVisualization dataset]] contain an MR scan and a series of 3D models of the brain.<br />
|align="right"|<br />
[[Image:Slicer4DataLoading_tutorial.png|right|250px|]]<br />
|}<br />
<br />
=Tutorials for software developers=<br />
<br />
== Slicer4 Programming Tutorial ==<br />
{|width="100%"<br />
|<br />
*The [https://www.dropbox.com/s/wrhrvvmplosiis1/Slicer4_ProgrammingTutorial_SPujol-SPieper_Nightly.pdf?dl=0# Slicer Programming tutorial] guides through the integration of a python module in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Steve Pieper, Ph.D.<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.7<br />
*The [https://www.dropbox.com/s/6yxu8qepmvywk0n/HelloPython_Nightly.zip?dl=0 HelloPython dataset] contains sample data set (MR scan of the brain) and complete Python module examples.<br />
|align="right"|<br />
[[Image:HelloPythonTutorial.png|right|250px|]]<br />
|}<br />
<br />
For additional Python scripts examples, please visit the [[Documentation/{{documentation/version}}/ScriptRepository|Script Repository page]]<br />
<br />
==Developing and contributing extensions for 3D Slicer==<br />
{|width="100%"<br />
|<br />
*The [http://goo.gl/IP4cdg Developing and contributing extensions for 3D Slicer tutorial ] is an introduction to the internals of 3D Slicer and the process of contributing a 3D Slicer extension.<br />
*Authors: Andrey Fedorov, Jean-Christophe Fillion-Robin, Steve Pieper<br />
*Audience: Developers<br />
*Based on: 3D Slicer version 4.4<br />
|align="right"|<br />
[[Image:Contributing3DSlicerExtension.png|right|250px|]]<br />
|}<br />
<br />
=Specific functions=<br />
<br />
==Slicer4 Diffusion Tensor Imaging Tutorial ==<br />
{|width="100%"<br />
|<br />
*Please visit [http://dmri.slicer.org/docs/ dmri.slicer.org/docs] for the latest documentation of SlicerDMRI.<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/DiffusionMRIanalysis.pdf Diffusion Tensor Imaging Tutorial] course guides through the basics of loading Diffusion Weighted images in Slicer, estimating tensors and generating fiber tracts. <br />
*Author: Sonia Pujol, Ph.D.<br />
*Audience: End-users and developers<br />
*Modules: Data, Volumes, DWI to DTI Estimation, Diffusion Tensor Scalar Measurements, Editor, Markups,Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:Dti tutorial data.zip|DTI dataset]] contains an MR Diffusion Weighted Imaging scan of the brain.<br />
|align="right"|<br />
[[Image:Slicer4DTI Tutorial.png|right|250px|]]<br />
|}<br />
<br />
==Slicer4 Neurosurgical Planning Tutorial==<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/WhiteMatterExplorationTutorial.pdf Neurosurgical Planning tutorial] course guides through the generation of fiber tracts in the vicinity of a tumor.<br />
*Author: Sonia Pujol, Ph.D., Ron Kikinis, M.D.<br />
*Audience: End-users and developers<br />
*Modules: Volumes, Editor, Tractography Label Map Seeding, Tractography Interactive Seeding<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:WhiteMatterExplorationData.zip| White Matter Exploration datasets]] contains a Diffusion Weighted Imaging scan of brain tumor patient.<br />
|align="right"|<br />
[[Image:NeurosurgicalPlanningTutorial.png|right|250px|link=http://vimeo.com/67336069]]<br />
|}<br />
<br />
==Slicer4 3D Visualization of DICOM images for Radiology Applications==<br />
{|width="100%"<br />
|<br />
*The [[Media:3DSlicer_Dicom_RSNA2015_SoniaPujol.pdf |3D Visualization of DICOM images for Radiology Applications]] course guides through 3D data loading and visualization of DICOM images for Radiology Applications in Slicer4. <br />
*Author: Sonia Pujol, Ph.D., Kitt Shaffer, M.D., Ph.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 visualization capabilities.<br />
*Modules: DICOM, Volumes, Volume Rendering, Models.<br />
*Based on: 3D Slicer version 4.5<br />
*The [[Media:3DVisualization_DICOM_images_part1.zip | 3DVisualizationDICOM_part1]] and [[Media:3DVisualization_DICOM_images_part2.zip | 3DVisualizationDICOM_part2]] datasets contain a series of MR and CT scans, and 3D models of the brain, lung and liver.<br />
|align="right"|<br />
[[Image:Slicer4RSNA_2.png|right|250px|]]<br />
|}<br />
<br />
==Slicer4 Quantitative Imaging tutorial==<br />
{|width="100%"<br />
|<br />
*The [[media:QuantitativeImaging_Slicer4.5.pdf | Slicer4 Quantitative Imaging tutorial]] guides through the use for Slicer for quantifying small volumetric changes in slow-growing tumors, and for calculating Standardized Uptake Value (SUV) from PET/CT data.<br />
*Authors: Sonia Pujol, Ph.D., Katarzyna Macura, M.D., Ron Kikinis, M.D.<br />
*Audience: Radiologists and users of Slicer who need a more comprehensive overview over Slicer4 quantitative imaging capabilities.<br />
*Modules: Data, Volumes, Models, Change Tracker, PET Standard Uptake Value Computation<br />
*Based on: 3D Slicer version 4.5<br />
*The [[media:QuantitativeImaging.zip| Quantitative Imaging dataset]] contains a series of MR and PET/CT data.<br />
|align="right"|<br />
[[Image:Slicer4_QuantitativeImaging.png|right|250px|]]<br />
|}<br />
<br />
== Slicer4 IGT ==<br />
{|width="100%"<br />
|<br />
*[http://www.slicerigt.org/wp/user-tutorial/ Slicer IGT tutorials]<br />
*Authors: Tamas Ungi, M.D, Ph.D., Junichi Tokuda, Ph.D.<br />
*Audience: End-users interested in using Slicer for real-time navigated procedures. E.g. navigated needle insertions or other minimally invasive medical procedures.<br />
*Modules: SlicerIGT Extension<br />
*Based on: Slicer4.3.1-2014.09.14<br />
*Data: [https://onedrive.live.com/redir?resid=7230D4DEC6058018!2937&authkey=!AGQkSCZOwjVYXw8&ithint=folder%2cpptx Slicer-IGT datasets]<br />
|align="right"|<br />
[[Image:SlicetIGT.png|right|150px|]]<br />
|}<br />
<br />
== Slicer4 3D Printing ==<br />
<br />
{|width="100%"<br />
|<br />
* The video tutorial [https://youtu.be/Uht6Fwtr9hE Segmenting a CT for 3D Printing of a Lumbar Phantom] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.7.<br />
** Author: Hillary Lia<br />
** Audience: Users and developers interested in 3D printing<br />
* The [https://www.slicer.org/wiki/Documentation/4.6/Training#Segmentation_for_3D_printing Segmentation for 3D printing] shows how to use the Segment Editor of 3D Slicer for 3D printing using Slicer 4.6.<br />
** Author: Csaba Pinter, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
* This ''Slicer 4.3 [https://www.youtube.com/watch?v=MKLWzD0PiIc 3D printing tutorial]'' shows how to prepare 3D Slicer data for 3D printing using legacy Editor module.<br />
** Authors: Nabgha Farhat, MSc<br />
** Audience: Users and developers interested in 3D printing<br />
|align="right"|[[Image:20170717_3DPrintingTutorialYoutube.PNG|280px]]<br />
|}<br />
<br />
== Slicer4 Image Registration ==<br />
<br />
{|width="100%"<br />
|<br />
*The [https://www.slicer.org/slicerWiki/index.php/File:RegistrationTutorial_3DSlicer4.5_spujol.pdf Registration tutorial] shows how to perform intra- and inter-subject registration within Slicer.<br />
* Authors: Sonia Pujol, Ph.D., Dominik Meier, Ph.D., Ron Kikinis, M.D.<br />
* Audience: Users and developers interested in image registration<br />
* Dataset: [[Media:RegistrationData.zip| 3D Slicer Registration Data]]<br />
|align="right"|[[File:registration_Slicer4.png|250px]]<br />
|}<br />
*Based on: 3D Slicer version 4.5<br />
See [[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|the Registration Library for worked out registration examples with data]].<br />
<br />
== Fast GrowCut ==<br />
<br />
{|width="100%"<br />
|<br />
* The [[media:FastGrowCutTutorial.pdf |Fast GrowCut tutorial]] shows how to perform a segmentation using the Fast GrowCut effect in Slicer.<br />
* Authors: Hillary Lia<br />
* Audience: Users interested in segmentation<br />
|align="right"|[[File:FastGrowCutLogo.png|200px]]<br />
|}<br />
<br />
==Slicer4 Radiation Therapy Tutorial ==<br />
** The [https://app.assembla.com/spaces/slicerrt/subversion/source/HEAD/trunk/SlicerRt/doc/tutorials/SlicerRT_TutorialIGRT_4.7.pdf?_format=raw SlicerRT tutorial] is an introduction to the Radiation Therapy functionalities of Slicer.<br />
** Author: Csaba Pinter, Andras Lasso, An Wang, Gregory C. Sharp, David Jaffray, Gabor Fichtinger. <br />
** Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip download] from MIDAS server<br />
**Based on Slicer 4.7<br />
<br />
== Other ==<br />
<br />
Additional (non-curated) videos-based demonstrations using 3D Slicer are accessible on [http://www.youtube.com/results?search_query=3d+slicer&sm=3 You Tube].<br />
<br />
= 3D Slicer Tutorial contests=<br />
<br />
==Winter 2017 Tutorial contest==<br />
<br />
===Segmentation for 3D printing===<br />
{|width="100%"<br />
|<br />
*The [https://www.assembla.com/spaces/slicerrt/documents/bmRQGEzzur54v-dmr6CpXy/download/bmRQGEzzur54v-dmr6CpXy Segmentation for 3D printing Tutorial] is an introduction to the new [[Documentation/{{documentation/version}}/Modules/SegmentEditor|Segment Editor]] module, demonstrated through the popular topic of 3D printing. <br />
*Author: Csaba Pinter (Queen's University, Canada)<br />
* [https://www.youtube.com/watch?v=Uht6Fwtr9hE Narrated video version on YouTube].<br />
*Dataset: [[:File:BasePiece.zip|Phantom base STL model]] Source: [http://perk-software.cs.queensu.ca/plus/doc/nightly/modelcatalog/ PerkLab].<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-Segmentation-for-3d-printing.png | 200px]]. <br />
|}<br />
<br />
===Slicer Pathology===<br />
{|width="100%"<br />
|<br />
*The [[Documentation/{{documentation/version}}/Extensions/SlicerPathology|Slicer Pathology Tutorial]] describes how to use the corresponding tools for automatic and semi-automatic pathology image segmentation.<br />
*Author: Erich Bremer (Stonybrook), Andriy Fedorov (Brigham and Women’s Hospital)<br />
*Dataset: Available directly with the Slicer Pathology Slicer extension.<br />
|align="right"|<br />
[[File:SlicerPathologyScreenShot8.png | 200px]]. <br />
|}<br />
<br />
===Simple Python Tool for Quality Control of DWI data===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/3/3a/SimpleDiffusionGradientInformationExtractorTutorial_Chauvin_Jan2017.pptx Simple Multi-shell Diffusion Gradients Information Extractor Tutorial] describes how to use a simple Python script for parsing multi-shell sensitizing gradients information from nifti file format (separated bvecs, bvals files).<br />
*Author: Laurent Chauvin (ETS Montreal)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SimpleDiffusionGradientInformationExtractorTutorial.png | 200px]]. <br />
|}<br />
<br />
===SPHARM-PDM===<br />
{|width="100%"<br />
|<br />
*The [https://www.nitrc.org/docman/view.php/308/1982/SPHARM-PDM_Tutorial_July2015.pdf SPHARM-PDM Tutorial] describes how to use SPHARM-PDM and ShapePopulationViewer Slicer extensions to respectively compute point-based models using a parametric boundary description for the computing of Shape Analysis and perform the quality control between the different models.<br />
*Author: Jonathan Perdomo (UNC), Beatriz Paniagua (Kitware Inc.)<br />
*Dataset: [https://www.nitrc.org/docman/view.php/308/1981/SPHARM_Tutorial_Data_July2015.zip Tutorial Data]<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-SPHARM-PDM.png | 200px]]. <br />
|}<br />
<br />
===Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*The [https://www.na-mic.org/Wiki/images/a/ab/ROSIGTLTutorial_Tokuda_Jan2017.pptx Integration of Robot Operating System (ROS) and 3D Slicer using OpenIGTLink Tutorial] describes the software architecture of surgical robot systems and allows to acquire hands-on experience of software-hardware integration for medical robotics.<br />
*Author: Junichi Tokuda (Brigham and Women’s Hospital)<br />
*Dataset: Not available.<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-Integration-ROS-3DSlicer-OpenIGTLink.png | 200px]]. <br />
|}<br />
<br />
===Fiber Bundle Volume Measurement===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/5/57/Fiber_Bundle_Volume_Measurement.pptx Fiber Bundle Volume Measurement Tutorial] aim is to calculate the volume of the fiber bundle that passes through the Corpus Callosum(CC). Following this tutorial, you’ll be able to (1) convert fiber bundles to label map and (2) calculate volume measurements from the fiber bundles.<br />
*Author: Shun Gong (Shanghai Changzheng Hospital, China)<br />
*Dataset: [http://www.na-mic.org/Wiki/images/4/4c/FiberVolume_data.zip Tutorial data]: The following data are provided: Baseline image, Down sampled whole brain tractography (conducted as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]] and down-sampled to about 10000 fibers using Tractography Display module), Corpus callosum label map (drawn as in the [[Documentation/{{documentation/version}}/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial|DWI tutorial]]).<br />
|align="right"|<br />
[[File:SlicerWinterProjectWeek2017-FiberBundleVolumeMeasurements.png | 200px]]. <br />
|}<br />
<br />
==Winter 2016 Tutorial contest==<br />
<br />
===Subject Hierarchy===<br />
{|width="100%"<br />
|<br />
*The [http://wiki.na-mic.org/Wiki/images/2/27/SubjectHierarchy.TutorialContestWinter2016.pdf Subject Hierarchy] tutorial demonstrates the basic usage and potential of Slicer’s data manager module Subject Hierarchy using a two-timepoint radiotherapy phantom dataset.<br />
*Author: Csaba Pinter, Queen's University, Canada<br />
*Dataset: [http://slicer.kitware.com/midas3/download/item/205404/SlicerRT_WorldCongress_TutorialIGRT_Dataset.zip SlicerRT_WorldCongress_TutorialIGRT_Dataset] The tutorial dataset is a two-timepoint phantom dataset taken from a RANDO head&neck phantom. It contains two studies, the planning one is a DICOM study consisting of a CT grayscale image and radiotherapy data: contours, dose distribution, treatment beams, plan information. The second timepoint consists of a CT NRRD volume and a dose NRRD volume.<br />
|align="right"|<br />
[[File:SubjectHierarchyTutorial.png | 200px]]. <br />
|}<br />
<br />
===Fiber Bundle Selection and Scalar Measurements===<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/FiberBundleSelectionAndScalarMeasurement.pdf Fiber Bundle Selection and Scalar Measurements] tutorial guides through the use of the Diffusion Bundle Selection module and the Fiber Tract Scalar Measurement module for diffusion MRI tractography data analysis.<br />
*Author: Fan Zhang, University of Sydney Australia and Brigham and Women's Hospital<br />
*Dataset: [[media:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016.zip| Fiber Bundle Selection And Scalar Measurement Tutorial Dataset]]<br />
|align="right"|<br />
[[File:FiberBundleSelectionAndScalarMeasurement_TutorialContestWinter2016_Snapshot.png|200px]]<br />
|}<br />
<br />
===Plastimatch ===<br />
{|width="100%"<br />
|<br />
*The [http://www.na-mic.org/Wiki/images/5/5c/Plastimatch_TutorialContestWinter2016.pdf Plastimatch tutorial] guides through registration and wrapping of DICOM and DICOM-RT data using the Plastimatch extension of 3D Slicer.<br />
*Author: Gregory Sharp, Massachusetts General Hospital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:Plastimatch_TutorialContestWinter2016.zip Plastimatch Tutorial Dataset]<br />
|align="right"|<br />
[[File:PlastimatchTutorial_Winter2016Contest.png|200px]]<br />
|}<br />
<br />
===UKF ===<br />
{|width="100%"<br />
|<br />
*The [https://github.com/SlicerDMRI/slicerdmri.github.io/raw/master/docs/tutorials/UKFTractography.pdf UKF tutorial] guides through the use of the Unscented Kalman Filter (UKF) tractography module. <br />
*Author: Pegah Kahali, Brigham and Women's Hopital<br />
*Dataset: [http://www.na-mic.org/Wiki/index.php/File:UKF-Tractography_TutorialContestWinter2016.zip UKF tutorial Dataset]<br />
|align="right"|<br />
[[File:UKF_Winter2016.png|200px]]<br />
|}<br />
<br />
==Summer 2014 Tutorial contest== <br />
<br />
===Cardiac Agatston Tutorial===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:TutorialContest_CardiacAgatstonScoring_2014.pdf Cardiac Agatston Scoring Tutorial]<br />
*Authors: Jessica Forbes, Hans Johnson, University of Iowa<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CardiacAgatstonMeasures_TutorialContestSummer2014.zip Cardiac Agatston Scoring Tutorial Dataset]<br />
|align="right"|<br />
[[File:CardiacAgatstonMeasuresModuleScreenshot.jpg| 250px]]<br />
|}<br />
<br />
===CMR Toolkit LA workflow===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflow_TutorialContestSummer2014.pdf CMR Toolkit LA Workflow Tutorial]<br />
*Authors: Salma Bengali, Josh Cates, University of Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CMRToolkitLAWorkflowData_TutorialContestSummer2014.zip CMRToolkitLAWorkflow Dataset]<br />
|align="right"|<br />
[[Image:Utah_SummerContest2014_tutorial.png|300px]]<br />
|}<br />
<br />
==Summer 2013 Tutorial contest==<br />
<br />
===Cardiac MRI Toolkit===<br />
{|width="100%"<br />
|<br />
*[[Media:Cardiac MRI Toolkit Tutorial Summer2013.pdf|Cardiac MRI Toolkit]]<br />
*Authors: Salma Bengali, Josh Cates, SCI, Utah<br />
*Dataset: [[Media:Cardiac_MRI_Toolkit_Tutorial_Data.zip|Cardiac MRI Toolkit Tutorial Dataset]]<br />
|align="right"|<br />
[[Image:CMRToolkit_Tutorial_Image.png|250px]]<br />
|}<br />
<br />
===HelloCLI===<br />
{|width="100%"<br />
|<br />
*[[Media:Hello_CLI_TutorialContestSummer2013.pdf|HelloCLI]]<br />
*Authors: Nadya Shusharina, Greg Sharp, MGH, Boston<br />
*Dataset: [[Media:Hello_CLI_TutorialContestSummer2013.zip|HelloCLI Dataset]]<br />
|align="right"|<br />
[[Image:Cli_icon.png|300px]]<br />
|}<br />
<br />
===SlicerRT===<br />
{|width="100%"<br />
|<br />
*[[Media:SlicerRT_TutorialContestSummer2013.pdf|SlicerRT Tutorial]]<br />
*Authors: Csaba Pinter, Andras Lasso (Queen's), Kevin Wang (PMH, Toronto)<br />
*Dataset: [[Media:CsabaPinter-SlicerRtTutorial_Namic2013June.zip|SlicerRT Dataset]] <br />
|align="right"|<br />
[[Image:667px-SlicerRT_0.10_IsocenterShiftingEvaluation.png|250px]]<br />
|}<br />
<br />
===DTIPrep===<br />
{|width="100%"<br />
|<br />
*[[Media:DTIPrep_TutorialContestSummer2013.pdf|DTIPrep]]<br />
*Authors: Dave Welch, SINAPSE, IOWA <br />
*Dataset: [[Media:DTIPrepData_TutorialContestSummer2013.zip|DTIPrep Dataset]]<br />
|align="right"|<br />
[[Image:DTIPrep-tutorial.png|250px]]<br />
|}<br />
<br />
== Summer 2012 Tutorial contest == <br />
<br />
===Automatic Left Atrial Scar Segmenter ===<br />
{|width="100%"<br />
|<br />
*[http://wiki.na-mic.org/Wiki/index.php/CARMA-LA-Scar_TutorialContestSummer2012 Automatic Left Atrial Scar Segmenter] <br />
*Authors: Greg Gardner, Josh Cates, SCI, Utah<br />
*Dataset: [http://wiki.na-mic.org/Wiki/index.php/File:CARMA-LA-Scar_TutorialContestSummer2012.zip CARMA-LA-Scar data]<br />
|align="right"|<br />
[[Image:Carma afib auto scar.png|250px]]<br />
|}<br />
<br />
===Qualitative and quantitative comparison of two RT dose distributions===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:PlastimatchDose_TutorialContestSummer2012.pdf Qualitative and quantitative comparison of two RT dose distributions]<br />
*Authors: James Shackleford, Nadya Shusharina, Greg Sharp, MGH<br />
|align="right"|<br />
[[Image:PlastimatchDose.png|250px]]<br />
|}<br />
<br />
===Dose accumulation for adaptive radiation therapy===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:DoseAccumulationforAdaptiveRadiationTherapy_TutorialContestSummer2012.pdf Dose accumulation for adaptive radiation therapy]<br />
*Authors: Kevin Wang, Csaba Pinter, Andras Lasso, PMH, Queen's<br />
|align="right"|<br />
[[Image:AdaptiveradiationTherapy.png|250px]]<br />
|}<br />
<br />
===WebGL Export===<br />
{|width="100%"<br />
|<br />
*[http://www.na-mic.org/Wiki/index.php/File:WebGLExport_TutorialContestSummer2012.pdf WebdGLExport]<br />
*Authors: Nicolas Rannou, Daniel Haehn, Children's Hospital<br />
|align="right"|<br />
[[Image:WebGLExport.png|250px]]<br />
|}<br />
<br />
===OpenIGTLink===<br />
{|width="100%"<br />
|<br />
*[http://wiki.slicer.org/slicerWiki/images/f/f1/OpenIGTLinkTutorial_Slicer4.1.0_JunichiTokuda_Apr2012.pdf OpenIGTLink]<br />
*Authors: Junichi Tokuda, BWH<br />
|align="right"|<br />
[[Image:OpenIGTLink.png|250px]]<br />
|}<br />
<br />
=Additional resources =<br />
{|width="100%"<br />
|<br />
* This ''Slicer 4.1 [http://vimeo.com/41096643 webinar]'' presents the new features and improvements of the release, and a brief overview of work for the next release.<br />
* Authors: Steve Pieper Ph.D.<br />
* Audience: First time users and developers interested in Slicer 4.1 new features.<br />
* Length: 0h20m<br />
|align="right"|[[Image:Webinar-Slicer-4.1.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*This ''Intro to Slicer 4.0 [http://vimeo.com/37671358 webinar]'' provides an introduction to 3DSlicer, and demonstrates core functionalities such as loading, visualizing and saving data. Basic processing tools, including manual registration, manual segmentation and tractography tools are also highlighted. This webinar is a general overview. For in depth information see the modules above and the documentation pages.<br />
*Authors: Julien Finet, M.S., Steve Pieper, Ph.D., Jean-Christophe Fillion-Robin, M.S. <br />
*Audience: First time users interested in a broad overview of Slicer’s features and tools.<br />
*Length: 1h20m<br />
|align="right"|[[Image:Webinar.png|250px]]<br />
|}<br />
<br><br />
----<br />
<br><br />
{|width="100%"<br />
|<br />
*The ''[[Documentation/{{documentation/version}}/Registration/RegistrationLibrary|Slicer Registration Case Library]]'' provides many real-life example cases of using the Slicer registration tools. They include the dataset and step-by-step instructions to follow and try yourself. <br />
:Author: Dominik Meier, Ph.D.<br />
:Audience: users interested learning/applying Slicer image registration technology<br />
|align="right"|[[Image:RegLib_table.png|250px|link=http://wiki.slicer.org/slicerWiki/index.php/Documentation/{{documentation/version}}/Registration/RegistrationLibrary]]<br />
|}<br />
<br />
= External Resources =<br />
<br />
== Murat Maga's blog posts about using 3D Slicer for biology ==<br />
<br />
* [https://blogs.uw.edu/maga/2017/04/11/getting-started-with-3d-slicer-as-a-biologist/ Slicer for Biologists]<br />
* [https://blogs.uw.edu/maga/2017/04/11/a-worked-example-getting-and-visualizing-data-from-digimorph/ Loading data from DigiMorph]<br />
* [https://blogs.uw.edu/maga/2017/04/11/morphosource-data-and-dealing-with-dicom-series-in-slicer/ Fixing problem DICOM]<br />
* [https://blogs.uw.edu/maga/2017/04/12/scissors-tool-is-awesome/ Scissors tool is awesom]<br />
<br />
== Using the (legacy) Editor ==<br />
<br />
This set of tutorials about the use of slicer in paleontology is very well written and provides step-by-step instructions. Even though it covers slicer version 3.4, many of the concepts and techniques have applicability to the new version and to any 3D imaging field:<br />
<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial.html Open Source Paleontologist: 3D Slicer: The Tutorial]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-ii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part II]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iii.html Open Source Paleontologist: 3D Slicer: The Tutorial Part III]<br />
* [http://openpaleo.blogspot.com/2008/12/3d-slicer-tutorial-part-iv.html Open Source Paleontologist: 3D Slicer: The Tutorial Part IV]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-v.html Open Source Paleontologist: 3D Slicer: The Tutorial Part V]<br />
* [http://openpaleo.blogspot.com/2009/03/3d-slicer-tutorial-part-vi.html Open Source Paleontologist: 3D Slicer: The Tutorial Part VI]<br />
<br />
== Team Contributions ==<br />
See the collection of videos on the [http://vimeo.com/album/2363361 Kitware vimeo album].<br />
<br />
== User Contributions ==<br />
See the [[Documentation/{{documentation/version}}/Training/UserContributions|User Contributions Page]] for more content.<br />
<br />
[http://www.youtube.com/results?search_query=3d+slicer&sm=3 YouTube videos about 3D Slicer]</div>Laurenhttps://www.slicer.org/w/index.php?title=Slicerverse&diff=52521Slicerverse2017-06-22T19:08:41Z<p>Lauren: </p>
<hr />
<div>__TOC__<br />
<br />
=Introduction=<br />
<br />
The SlicerVerse represents a collection of customized versions or plug-ins for Slicer that respond to the needs of a certain research community.<br />
<br />
=Requirements for inclusion=<br />
<br />
Having an open-source repository that presents a Slicer software solution with specialized functionality. <br />
It needs to be a live project with a contact person responsible for answering user questions.<br />
<br />
If you wish to be included in the community, please send a short description, icon, institute, SlicerVerse solution name, main webpage link as well as a tutorials webpage link.</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerDMRI&diff=47253Documentation/Nightly/Extensions/SlicerDMRI2016-10-11T18:42:15Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{|<br />
|<br />
[[Image:DMRI 3D SLICER-icon.png]]<br />
|<br />
Extension: [[Documentation/{{documentation/version}}/Extensions/SlicerDMRI|SlicerDMRI]]<br><br />
Acknowledgments:<br />
This work is supported in part by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through the following grants:<br />
* NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research)<br />
* NIH P41EB015898 (National Center for Image-Guided Therapy)<br />
* NIH P41EB015902 (Neuroimaging Analysis Center)<br />
* National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.<br />
Major Contributors: Lauren J. O'Donnell ({{collaborator|name|spl}}), Isaiah Norton (Brigham and Women's Hospital), Raul San Jose Estepar (Brigham and Women's Hospital), Carl-Fredrik Westin (Brigham and Women's Hospital), Alex Yarmarkovich<br><br />
<br />
License: [http://www.slicer.org/pages/LicenseText Slicer License]<br />
|}<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:NCIGT-Logo.jpeg|NCIGT<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Extension Description}}<br />
<br />
SlicerDMRI extension hosts various modules to facilitate <br />
* processing and management of diffusion MRI image data<br />
* utilizing diffusion MRI tractography for neuroscience studies and for planning image-guided interventions<br />
* quantitative analysis of diffusion MRI data<br />
<br />
{{documentation/{{documentation/version}}/extension-section|Modules}}<br />
<br />
*[[Documentation/{{documentation/version}}/Modules/FiberTractMeasurements |Fiber Tract Measurements]]: module to quantify diffusion measures from fiber tracts<br />
<br />
<br />
{{documentation/{{documentation/version}}/extension-section|References}}<br />
[1] O’Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurgery clinics of North America. 2011 Apr 30;22(2):185-96.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Information for Developers}}<br />
* SlicerSlicerDMRI organization page on github: https://github.com/SlicerDMRI<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
[[Category:Documentation/{{documentation/version}}/Modules/Diffusion]]<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerDMRI&diff=47252Documentation/Nightly/Extensions/SlicerDMRI2016-10-11T18:38:41Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{|<br />
|<br />
[[Image:DMRI 3D SLICER-icon.png]]<br />
|<br />
Extension: [[Documentation/{{documentation/version}}/Extensions/SlicerDMRI|SlicerDMRI]]<br><br />
Acknowledgments:<br />
This work is supported in part by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through the following grants:<br />
* NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research)<br />
* NIH P41EB015898 (National Center for Image-Guided Therapy)<br />
* NIH P41EB015902 (Neuroimaging Analysis Center)<br />
* National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.<br />
Major Contributors: Lauren J. O'Donnell ({{collaborator|name|spl}}), Isaiah Norton (Brigham and Women's Hospital), Raul San Jose Estepar (Brigham and Women's Hospital), Carl-Fredrik Westin (Brigham and Women's Hospital), Alex Yarmarkovich<br><br />
<br />
License: [http://www.slicer.org/pages/LicenseText Slicer License]<br />
|}<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:NCIGT-Logo.jpeg|NCIGT<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Extension Description}}<br />
<br />
SlicerDMRI extension hosts various modules to facilitate <br />
* processing and management of diffusion MRI image data<br />
* utilizing diffusion MRI tractography for neuroscience studies and for planning image-guided interventions<br />
* quantitative analysis of diffusion MRI data<br />
<br />
{{documentation/{{documentation/version}}/extension-section|Modules}}<br />
<br />
*[[Documentation/{{documentation/version}}/Modules/FiberTractMeasurements |Fiber tract Measurements]: module to quantify diffusion measures from fiber tracts<br />
<br />
<br />
{{documentation/{{documentation/version}}/extension-section|References}}<br />
[1] O’Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurgery clinics of North America. 2011 Apr 30;22(2):185-96.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Information for Developers}}<br />
* SlicerSlicerDMRI organization page on github: https://github.com/SlicerDMRI<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
[[Category:Documentation/{{documentation/version}}/Modules/Diffusion]]<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerDMRI&diff=47251Documentation/Nightly/Extensions/SlicerDMRI2016-10-11T18:38:00Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{|<br />
|<br />
[[Image:DMRI 3D SLICER-icon.png]]<br />
|<br />
Extension: [[Documentation/{{documentation/version}}/Extensions/SlicerDMRI|SlicerDMRI]]<br><br />
Acknowledgments:<br />
This work is supported in part by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through the following grants:<br />
* NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research)<br />
* NIH P41EB015898 (National Center for Image-Guided Therapy)<br />
* NIH P41EB015902 (Neuroimaging Analysis Center)<br />
* National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.<br />
Major Contributors: Lauren J. O'Donnell ({{collaborator|name|spl}}), Isaiah Norton (Brigham and Women's Hospital), Raul San Jose Estepar (Brigham and Women's Hospital), Carl-Fredrik Westin (Brigham and Women's Hospital)<br><br />
<br />
License: [http://www.slicer.org/pages/LicenseText Slicer License]<br />
|}<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:NCIGT-Logo.jpeg|NCIGT<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Extension Description}}<br />
<br />
SlicerDMRI extension hosts various modules to facilitate <br />
* processing and management of diffusion MRI image data<br />
* utilizing diffusion MRI tractography for neuroscience studies and for planning image-guided interventions<br />
* quantitative analysis of diffusion MRI data<br />
<br />
{{documentation/{{documentation/version}}/extension-section|Modules}}<br />
<br />
*[[Documentation/{{documentation/version}}/Modules/FiberTractMeasurements |Fiber tract Measurements]: module to quantify diffusion measures from fiber tracts<br />
<br />
<br />
{{documentation/{{documentation/version}}/extension-section|References}}<br />
[1] O’Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurgery clinics of North America. 2011 Apr 30;22(2):185-96.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Information for Developers}}<br />
* SlicerSlicerDMRI organization page on github: https://github.com/SlicerDMRI<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
[[Category:Documentation/{{documentation/version}}/Modules/Diffusion]]<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerDMRI&diff=47250Documentation/Nightly/Extensions/SlicerDMRI2016-10-11T18:32:45Z<p>Lauren: Created page with "<noinclude>{{documentation/versioncheck}}</noinclude> <!-- ---------------------------- --> {{documentation/{{documentation/version}}/module-header}} <!-- --------------------..."</p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{|<br />
|<br />
[[Image:SlicerProstate_Logo_1.0_128x128.png]]<br />
|<br />
Extension: [[Documentation/{{documentation/version}}/Extensions/SlicerDMRI|SlicerDMRI]]<br><br />
Acknowledgments:<br />
This work is supported in part by the National Cancer Institute and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through the following grants:<br />
* NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research)<br />
* NIH P41EB015898 (National Center for Image-Guided Therapy)<br />
* NIH P41EB015902 (Neuroimaging Analysis Center)<br />
* National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.<br />
Major Contributors: Lauren J. O'Donnell ({{collaborator|name|spl}}), Isaiah Norton (Brigham and Women's Hospital), Raul San Jose Estepar (Brigham and Women's Hospital), Carl-Fredrik Westin (Brigham and Women's Hospital)<br><br />
<br />
License: [http://www.slicer.org/pages/LicenseText Slicer License]<br />
|}<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:NCIGT-Logo.jpeg|NCIGT<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Extension Description}}<br />
<br />
SlicerDMRI extension hosts various modules to facilitate <br />
* processing and management of diffusion MRI image data<br />
* utilizing diffusion MRI tractography for neuroscience studies and for planning image-guided interventions<br />
* quantitative analysis of diffusion MRI data<br />
<br />
{{documentation/{{documentation/version}}/extension-section|Modules}}<br />
<br />
*[[Documentation/{{documentation/version}}/Modules/FiberTractMeasurements |Fiber tract Measurements]: module to quantify diffusion measures from fiber tracts<br />
<br />
<br />
{{documentation/{{documentation/version}}/extension-section|References}}<br />
[1] O’Donnell LJ, Westin CF. An introduction to diffusion tensor image analysis. Neurosurgery clinics of North America. 2011 Apr 30;22(2):185-96.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/extension-section|Information for Developers}}<br />
* SlicerSlicerDMRI organization page on github: https://github.com/SlicerDMRI<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
[[Category:Documentation/{{documentation/version}}/Modules/Diffusion]]<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=File:NCIGT-Logo.jpeg&diff=47249File:NCIGT-Logo.jpeg2016-10-11T18:23:19Z<p>Lauren: </p>
<hr />
<div></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Announcements&diff=47248Documentation/Nightly/Announcements2016-10-11T18:17:33Z<p>Lauren: /* Slicer {{documentation/version}} Highlights */</p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
__NOTOC__<br />
<br />
<br />
<br />
{|width="100%"<br />
|align="center"|<br />
[[image:Slicer4Announcement-HiRes.png|center|'''{{documentation/version}}''']]<br />
|}<br />
<br />
<br />
{|align="center" border="1" style="text-align:center; font-size:120%; border-spacing: 0; padding: 0px;" cellpadding="10"<br />
|[[#Summary|Summary ]]<br />
|[[#What is 3D Slicer|What is 3D Slicer]]<br />
|[[#Slicer {{documentation/version}} Highlights|Slicer {{documentation/version}} Highlights]]<br />
|[[#Slicer Training|Slicer Training]]<br />
|[[#Slicer Extensions|Slicer Extensions]]<br />
|[[#Other Improvements, Additions & Documentation|Other Improvements, Additions & Documentation]]<br />
|}<br />
<br />
= Summary = <br />
<br />
The community of Slicer developers is proud to announce the release of '''Slicer {{documentation/version}}'''.<br />
<br />
<br><hr><br><br />
<br />
The development of Slicer, including its numerous modules, extensions, datasets, patches sent on user and developer lists, issues reports, suggestions, ... is made possible by awesome users, developers, contributors, commercial partners from around the world and also invaluable grants and funding agencies.<br />
<br />
For more details, see [[Documentation/{{documentation/version}}/Acknowledgments|Acknowledgments]] page.<br />
<br />
<br><hr><br><br />
<br />
* Slicer {{documentation/version}} introduces <br />
<!--<br />
** An improved App Store, known as the Extension Manager, for adding plug-ins to Slicer. More than 80 plug-ins and packages of plug-ins are currently available.<br />
--><br />
** Close to XXX feature improvements and bug fixes have resulted in improved performance and stability.<br />
** Improvements to many modules.<br />
<br />
* Click here to [http://download.slicer.org/ download] Slicer {{documentation/version}} for different platforms and find pointers to the source code, mailing lists and the bug tracker. <br />
* Please note that Slicer continues to be a research package and is not intended for clinical use. Testing of functionality is an ongoing activity with high priority, however, some features of Slicer are not fully tested.<br />
* The [[Documentation/{{documentation/version}}/Training|Slicer Training]] page provides a series of tutorials and data sets for training in the use of Slicer. <br />
<br />
* [http://www.slicer.org slicer.org] is the portal to the application, training materials, and the development community.<br />
<br />
= What is 3D Slicer =<br />
{{:Documentation/{{documentation/version}}/Slicer}}<br />
<br />
= Citing Slicer =<br />
{{:Documentation/{{documentation/version}}/Acknowledgments/CitingSlicer}}<br />
<br />
= Slicer {{documentation/version}} Highlights =<br />
<br />
<gallery caption="New and Improved Modules" widths="350px" heights="250px" perrow="3"><br />
<br />
Image:SlicerDMRIScreenshot.jpg| Introduced [[Documentation/Nightly/Extensions/SlicerDMRI|SlicerDMRI]] extension including diffusion MRI modules formerly in Slicer core and new functionality:<ul><li>DICOM tractography import/export</li><li>Improved diffusion brain mask generation</li><li>UKF multi-fiber tractography now available on Windows</li><li>Improved user interface and documentation</li></ul><br />
<br />
Image:SlicerProstate_Logo_1.0_128x128.png| Improved [[Documentation/Nightly/Extensions/SlicerProstate|SlicerProstate]] extension.<ul><li>improved reporting of DWI model fit diagnostics</li><li>refactoring of the code to separate functionality common to mpReview and SliceTracker extensions into a reusable library</li></ul><br />
<br />
Image:MpReview-Prostate.gif|New [[Documentation/Nightly/Extensions/mpReview|mpReview]] extension to support review and annotation of multiparametric image data. The driving use case for the development of this module was review and segmentation of the regions of interest in prostate cancer multiparametric MRI.<br />
<br />
Image:Needle_tracking.png| New [[Documentation/Nightly/Extensions/SliceTracker|SliceTracker]] extension to support navigation and guidance during in-bore MRI-guided prostate biopsy. Main documentation for this module is hosted on Gitbook - give us your feedback about this approach for documenting Slicer functionality! https://fedorov.gitbooks.io/slicetracker/content/<br />
<br />
Image:SlicerPathologyScreenShot9.png|New [[Documentation/Nightly/Extensions/SlicerPathology|SlicerPathology]] extension for tools for automatic and semi-automatic pathology image segmentation, with the interface to [http://imaging.cci.emory.edu/wiki/display/CAMIC/Home caMicroscope].<br />
<br />
</gallery><br />
<br />
= Slicer Training =<br />
<br />
The [[Documentation/{{documentation/version}}/Training|Slicer Training]] page provides a series of updated tutorials and data sets for training in the use of Slicer {{documentation/version}}. <br />
<br />
The first 3D Slicer training events using Slicer 4.5 will be organized at [http://www.na-mic.org/Wiki/index.php/MGH_2015 Massachusetts General Hospital (MGH), Boston, MA], [http://www.na-mic.org/Wiki/index.php/Brown_2015 Brown University, Providence, RI] and [http://www.na-mic.org/Wiki/index.php/RSNA_2015 RSNA 2015, Chicago, Il].<br />
<br />
<gallery caption="New Tutorials" widths="250px" heights="150px"><br />
<br />
Image:SlicerYouTube.png| [https://www.youtube.com/channel/UC11x1iQ7ydSIFYw4L6wveXg?view_as=public 3D Slicer YouTube channel] has been reorganized, new videos developed by the 3D Slicer community added to the channel {{new}}<br />
<br />
<br />
<!-- You could user either {{new}} or {{updated}} macros. --><br />
<br />
</gallery><br />
<br />
=Slicer Extensions=<br />
<br />
<gallery caption="New Extensions" widths="250px" heights="150px"><br />
<br />
Image:AnglePlanes Logo.png|[[Documentation/{{documentation/version}}/Extensions/AnglePlanes|AnglePlanes]] This Module is used to calculate the angle between two planes by using the normals {{new}}<br />
<br />
<!-- You could user either {{new}} or {{updated}} macros. --><br />
</gallery><br />
<br />
== Improved Extensions in Slicer 4.5 ==<br />
<br />
* [[Documentation/{{documentation/version}}/Extensions/CMFreg|CMFreg]]<br />
<!-- Add entry here --><br />
<br />
== Extensions removed from Slicer 4.5 ==<br />
<br />
<!-- * houghTransformCLI: Removed by the original author because it was not needed anymore. --><br />
<br />
== Extensions renamed ==<br />
<br />
<!-- <br />
* PyDevRemoteDebug -> [[Documentation/{{documentation/version}}/Extensions/DebuggingTools|DebuggingTools]]<br />
* MultidimData -> [[Documentation/{{documentation/version}}/Extensions/Sequences|Sequences]]<br />
* TrackerStabilizer -> [[Documentation/{{documentation/version}}/Extensions/TrackerStabilizer|Slicer-TrackerStabilizer]]<br />
* AirwaySegmentation -> [[Documentation/{{documentation/version}}/Extensions/AirwaySegmentation|Slicer-AirwaySegmentation]]<br />
--><br />
<br />
= Other Improvements, Additions & Documentation =<br />
<br />
== Optimization ==<br />
<br />
== Transforms ==<br />
<br />
== DICOM ==<br />
<br />
== Data processing ==<br />
<br />
== CLI ==<br />
<br />
== Usability ==<br />
<br />
== SubjectHierarchy ==<br />
<br />
== Python scripting ==<br />
<br />
== Editor ==<br />
<br />
== Markups ==<br />
<br />
== LabelMapVolumeNode ==<br />
<br />
== Slice viewers ==<br />
<br />
== DataProbe ==<br />
<br />
== SliceViewAnnotations ==<br />
<br />
== OpenIGTLink ==<br />
<br />
<br />
= For Developers =<br />
<br />
== Modules and Extensions ==<br />
<br />
<!-- <br />
* ExtensionWizard<br />
<br />
* SelfTests<br />
** Allow self tests to set a custom delay for message display<br />
<br />
* MRMLNodeComboBox<br />
** Allow qMRMLNodeComboBox base name setting for each node type <br />
** Added removeAttribute function to MRML node comboboxes and proxy model <br />
** Multiple node types can be created in qMRMLNodeComboBox <br />
** Support custom behavior for default actions. <br />
<br />
* MRML Scene introspection<br />
** Added node printself output to node inspector <br />
<br />
* Message logging<br />
** Added vtkInfoMacro<br />
<br />
* DICOM<br />
** Package additional DCMTK applications: echoscu, dsr2html, xml2dcm and xml2dsr<br />
<br />
* Markups<br />
** Signal end fiducial interaction in 3D <br />
** Add reusable simple markups widget <br />
<br />
* Module API<br />
** Extend module API with "widgetRepresentationCreationEnabled" property<br />
<br />
* Slicerlets<br />
** Allow slicelets and Slicer tests to handle log messages. <br />
<br />
* Units<br />
** Added extra units support: frequency, velocity and intensity.<br />
** Extended Units logic API adding GetDisplayCoefficient() and GetSIPrefixCoefficient() <br />
** Updated MRMLUnitNode to ensure value to string conversion account for precision. <br />
<br />
* SubjectHierarchy<br />
** Introduced adaptor classes facilitating implementation of python scripted subject hierarchy plugins. <br />
** Introduced autoDeleteSubjectHierarchyChildren property <br />
<br />
* Editor<br />
** Facilitate re-use of Editor python components in extension (LabelStructureListWidget in 043f398)<br />
** Updated EditUtil API adding function SetUseLabelOutline() to explicitly set label outline state on all Slice nodes<br />
--><br />
<br />
== Slicer Core ==<br />
<br />
<!-- <br />
* IDE integration<br />
** Improve build targets organization in IDE that support folders.<br />
** VisualStudio: Do not build documentation when F7 is pressed.<br />
** Facilitate integration with python IDE allowing minimal 'slicer' module to be imported.<br />
<br />
* Build-system<br />
** Add support for Visual Studio 2013.<br />
** Update build system anticipating transition to modern CMake.<br />
** Update MacOSX packaging infrastructure anticipating the signing of package in future release.<br />
** Update code base anticipitating compliance with C++11.<br />
** Remove <code>Slicer_ITKV3_COMPATIBILITY</code> build option.<br />
** Add option <code>Slicer_USE_ITKPython</code> to turn on ITK Python wrapping.<br />
** Removed duplicated code using ITK version of MGHIO.<br />
<br />
* Python<br />
** Added support writing scripted modules and widgets as new-style Python classes. <br />
** Simplify scripted module introducing SlicerPythonCppAPI. <br />
** Introduced qSlicerScriptedUtils::executeFile() <br />
** Updated CTK to include new PythonQt C++/Python ownership tracking feature. <br />
** Import scripted module as python module to avoid module top-level variables to clobber each other. [http://www.na-mic.org/Bug/view.php?id=3549 #3549]<br />
<br />
* Platform support<br />
** Fix support for 32-bit build allowing use of Slicer on Surface tablet.<br />
<br />
* Packaging & Testing infrastructure<br />
** Capturing VTK errors/warnings during testing.<br />
** Update extension build system to report packaging error on CDash.<br />
<br />
* Rendering / Visualization<br />
** Add Slicer_VTK_RENDERING_BACKEND configure option<br />
<br />
* MRML<br />
** Add support in vtkMRMLNode for multiple references to the same node. <br />
** Introducing InvokeCustomModifiedEvent. <br />
<br />
* ApplicationLogic<br />
** Added function PropagateLabelVolumeSelection(), PropagateForegroundVolumeSelection() and PropagateBackgroundVolumeSelection()<br />
<br />
* VolumeRenderingLogic<br />
** Extend volume rendering logic API adding GetPresetByName function. <br />
<br />
* View management<br />
** Add qMRMLLayoutViewFactory <br />
** Add utility method to display node in only 1 view <br />
<br />
* DICOM<br />
** [https://github.com/Slicer/Slicer/pull/359 upgraded DCMTK to the latest snapshot DCMTK-3.6.1_20150924]<br />
--><br />
<br />
<gallery caption="Improved Toolkits" widths="350px" heights="250px" perrow="3"><br />
<br />
Image:CTK-Logo.png|Moved from CTK [https://github.com/commontk/CTK/commit/f64b68a f64b68a] to [https://github.com/commontk/CTK/commit/1c97e54 1c97e54] (499 commits) <!-- git log --oneline f64b68acd717dab060db41e8bee3f0f30df1a58f...1c97e5426f898bc7d074e6122992d0dd12bab56b --no-merges | wc -l --><br />
<br />
Image:CTKApplauncher_Logo.png|Moved from CTKAppLauncher v0.1.11 to v0.1.14 (43 commits) <!-- git log --oneline v0.1.11..v0.1.14 --no-merges | wc -l --><br />
<br />
Image:ITK_logo.png|Moved from ITK v4.4.1 to v4.6.0 (1089 commits) <!-- git log --oneline v4.4.1..56fae27 --no-merges | wc -l --><br />
<br />
Image:OpenIGTLink-Logo.png|Moved from OpenIGTLink [https://github.com/openigtlink/OpenIGTLink/compare/66e272d...849b434 66e272d to 849b434] (53 commits) <!-- git log --no-merges --oneline 66e272d..849b434 | wc -l --><br />
<br />
Image:Qt-logo.png |Moved from Qt 4.7.4 to Qt 4.8.6<br />
<br />
Image:VTK_logo.png|Moved from VTK v5.10.1 to VTK v6.2.0 (5490 commits) <!-- git log --oneline v5.10.1..b55dad7 --no-merges | wc -l --><br />
<br />
Image:DCMTK_logo.png|[https://github.com/Slicer/Slicer/pull/359 Upgraded DCMTK to DCMTK-3.6.1_20150924 snapshot]<br />
<br />
</gallery><br />
<br />
== Looking at the Code Changes ==<br />
<br />
From a git checkout you can easily see the all the commits since the time of the 4.4.0 release:<br />
<br />
git log v4.4.0..HEAD<br />
<br />
To see a summary of your own commits, you could use something like:<br />
<br />
git log v4.4.0..HEAD --oneline --author=me<br />
<br />
see [https://www.kernel.org/pub/software/scm/git/docs/git-log.html the git log man page] for more options.<br />
<br />
[[Release_Details#Slicer_{{documentation/version}}.0|Commit stats and full changelog]]<br />
<br />
= Related Projects =<br />
<gallery caption="" widths="250px" heights="150px" perrow="3"><br />
</gallery></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Announcements&diff=47247Documentation/Nightly/Announcements2016-10-11T18:14:42Z<p>Lauren: /* Slicer {{documentation/version}} Highlights */</p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
__NOTOC__<br />
<br />
<br />
<br />
{|width="100%"<br />
|align="center"|<br />
[[image:Slicer4Announcement-HiRes.png|center|'''{{documentation/version}}''']]<br />
|}<br />
<br />
<br />
{|align="center" border="1" style="text-align:center; font-size:120%; border-spacing: 0; padding: 0px;" cellpadding="10"<br />
|[[#Summary|Summary ]]<br />
|[[#What is 3D Slicer|What is 3D Slicer]]<br />
|[[#Slicer {{documentation/version}} Highlights|Slicer {{documentation/version}} Highlights]]<br />
|[[#Slicer Training|Slicer Training]]<br />
|[[#Slicer Extensions|Slicer Extensions]]<br />
|[[#Other Improvements, Additions & Documentation|Other Improvements, Additions & Documentation]]<br />
|}<br />
<br />
= Summary = <br />
<br />
The community of Slicer developers is proud to announce the release of '''Slicer {{documentation/version}}'''.<br />
<br />
<br><hr><br><br />
<br />
The development of Slicer, including its numerous modules, extensions, datasets, patches sent on user and developer lists, issues reports, suggestions, ... is made possible by awesome users, developers, contributors, commercial partners from around the world and also invaluable grants and funding agencies.<br />
<br />
For more details, see [[Documentation/{{documentation/version}}/Acknowledgments|Acknowledgments]] page.<br />
<br />
<br><hr><br><br />
<br />
* Slicer {{documentation/version}} introduces <br />
<!--<br />
** An improved App Store, known as the Extension Manager, for adding plug-ins to Slicer. More than 80 plug-ins and packages of plug-ins are currently available.<br />
--><br />
** Close to XXX feature improvements and bug fixes have resulted in improved performance and stability.<br />
** Improvements to many modules.<br />
<br />
* Click here to [http://download.slicer.org/ download] Slicer {{documentation/version}} for different platforms and find pointers to the source code, mailing lists and the bug tracker. <br />
* Please note that Slicer continues to be a research package and is not intended for clinical use. Testing of functionality is an ongoing activity with high priority, however, some features of Slicer are not fully tested.<br />
* The [[Documentation/{{documentation/version}}/Training|Slicer Training]] page provides a series of tutorials and data sets for training in the use of Slicer. <br />
<br />
* [http://www.slicer.org slicer.org] is the portal to the application, training materials, and the development community.<br />
<br />
= What is 3D Slicer =<br />
{{:Documentation/{{documentation/version}}/Slicer}}<br />
<br />
= Citing Slicer =<br />
{{:Documentation/{{documentation/version}}/Acknowledgments/CitingSlicer}}<br />
<br />
= Slicer {{documentation/version}} Highlights =<br />
<br />
<gallery caption="New and Improved Modules" widths="350px" heights="250px" perrow="3"><br />
<br />
Image:DMRI_3D_SLICER-icon.png | Introduced [[Documentation/Nightly/Extensions/SlicerDMRI|SlicerDMRI]] extension including diffusion MRI modules formerly in Slicer core and new functionality:<ul><li>DICOM tractography import/export</li><li>Improved diffusion brain mask generation</li><li>UKF multi-fiber tractography now available on Windows</li><li>Improved user interface and documentation</li></ul><br />
<br />
Image:SlicerProstate_Logo_1.0_128x128.png| Improved [[Documentation/Nightly/Extensions/SlicerProstate|SlicerProstate]] extension.<ul><li>improved reporting of DWI model fit diagnostics</li><li>refactoring of the code to separate functionality common to mpReview and SliceTracker extensions into a reusable library</li></ul><br />
<br />
Image:MpReview-Prostate.gif|New [[Documentation/Nightly/Extensions/mpReview|mpReview]] extension to support review and annotation of multiparametric image data. The driving use case for the development of this module was review and segmentation of the regions of interest in prostate cancer multiparametric MRI.<br />
<br />
Image:Needle_tracking.png| New [[Documentation/Nightly/Extensions/SliceTracker|SliceTracker]] extension to support navigation and guidance during in-bore MRI-guided prostate biopsy. Main documentation for this module is hosted on Gitbook - give us your feedback about this approach for documenting Slicer functionality! https://fedorov.gitbooks.io/slicetracker/content/<br />
<br />
Image:SlicerPathologyScreenShot9.png|New [[Documentation/Nightly/Extensions/SlicerPathology|SlicerPathology]] extension for tools for automatic and semi-automatic pathology image segmentation, with the interface to [http://imaging.cci.emory.edu/wiki/display/CAMIC/Home caMicroscope].<br />
<br />
</gallery><br />
<br />
= Slicer Training =<br />
<br />
The [[Documentation/{{documentation/version}}/Training|Slicer Training]] page provides a series of updated tutorials and data sets for training in the use of Slicer {{documentation/version}}. <br />
<br />
The first 3D Slicer training events using Slicer 4.5 will be organized at [http://www.na-mic.org/Wiki/index.php/MGH_2015 Massachusetts General Hospital (MGH), Boston, MA], [http://www.na-mic.org/Wiki/index.php/Brown_2015 Brown University, Providence, RI] and [http://www.na-mic.org/Wiki/index.php/RSNA_2015 RSNA 2015, Chicago, Il].<br />
<br />
<gallery caption="New Tutorials" widths="250px" heights="150px"><br />
<br />
Image:SlicerYouTube.png| [https://www.youtube.com/channel/UC11x1iQ7ydSIFYw4L6wveXg?view_as=public 3D Slicer YouTube channel] has been reorganized, new videos developed by the 3D Slicer community added to the channel {{new}}<br />
<br />
<br />
<!-- You could user either {{new}} or {{updated}} macros. --><br />
<br />
</gallery><br />
<br />
=Slicer Extensions=<br />
<br />
<gallery caption="New Extensions" widths="250px" heights="150px"><br />
<br />
Image:AnglePlanes Logo.png|[[Documentation/{{documentation/version}}/Extensions/AnglePlanes|AnglePlanes]] This Module is used to calculate the angle between two planes by using the normals {{new}}<br />
<br />
<!-- You could user either {{new}} or {{updated}} macros. --><br />
</gallery><br />
<br />
== Improved Extensions in Slicer 4.5 ==<br />
<br />
* [[Documentation/{{documentation/version}}/Extensions/CMFreg|CMFreg]]<br />
<!-- Add entry here --><br />
<br />
== Extensions removed from Slicer 4.5 ==<br />
<br />
<!-- * houghTransformCLI: Removed by the original author because it was not needed anymore. --><br />
<br />
== Extensions renamed ==<br />
<br />
<!-- <br />
* PyDevRemoteDebug -> [[Documentation/{{documentation/version}}/Extensions/DebuggingTools|DebuggingTools]]<br />
* MultidimData -> [[Documentation/{{documentation/version}}/Extensions/Sequences|Sequences]]<br />
* TrackerStabilizer -> [[Documentation/{{documentation/version}}/Extensions/TrackerStabilizer|Slicer-TrackerStabilizer]]<br />
* AirwaySegmentation -> [[Documentation/{{documentation/version}}/Extensions/AirwaySegmentation|Slicer-AirwaySegmentation]]<br />
--><br />
<br />
= Other Improvements, Additions & Documentation =<br />
<br />
== Optimization ==<br />
<br />
== Transforms ==<br />
<br />
== DICOM ==<br />
<br />
== Data processing ==<br />
<br />
== CLI ==<br />
<br />
== Usability ==<br />
<br />
== SubjectHierarchy ==<br />
<br />
== Python scripting ==<br />
<br />
== Editor ==<br />
<br />
== Markups ==<br />
<br />
== LabelMapVolumeNode ==<br />
<br />
== Slice viewers ==<br />
<br />
== DataProbe ==<br />
<br />
== SliceViewAnnotations ==<br />
<br />
== OpenIGTLink ==<br />
<br />
<br />
= For Developers =<br />
<br />
== Modules and Extensions ==<br />
<br />
<!-- <br />
* ExtensionWizard<br />
<br />
* SelfTests<br />
** Allow self tests to set a custom delay for message display<br />
<br />
* MRMLNodeComboBox<br />
** Allow qMRMLNodeComboBox base name setting for each node type <br />
** Added removeAttribute function to MRML node comboboxes and proxy model <br />
** Multiple node types can be created in qMRMLNodeComboBox <br />
** Support custom behavior for default actions. <br />
<br />
* MRML Scene introspection<br />
** Added node printself output to node inspector <br />
<br />
* Message logging<br />
** Added vtkInfoMacro<br />
<br />
* DICOM<br />
** Package additional DCMTK applications: echoscu, dsr2html, xml2dcm and xml2dsr<br />
<br />
* Markups<br />
** Signal end fiducial interaction in 3D <br />
** Add reusable simple markups widget <br />
<br />
* Module API<br />
** Extend module API with "widgetRepresentationCreationEnabled" property<br />
<br />
* Slicerlets<br />
** Allow slicelets and Slicer tests to handle log messages. <br />
<br />
* Units<br />
** Added extra units support: frequency, velocity and intensity.<br />
** Extended Units logic API adding GetDisplayCoefficient() and GetSIPrefixCoefficient() <br />
** Updated MRMLUnitNode to ensure value to string conversion account for precision. <br />
<br />
* SubjectHierarchy<br />
** Introduced adaptor classes facilitating implementation of python scripted subject hierarchy plugins. <br />
** Introduced autoDeleteSubjectHierarchyChildren property <br />
<br />
* Editor<br />
** Facilitate re-use of Editor python components in extension (LabelStructureListWidget in 043f398)<br />
** Updated EditUtil API adding function SetUseLabelOutline() to explicitly set label outline state on all Slice nodes<br />
--><br />
<br />
== Slicer Core ==<br />
<br />
<!-- <br />
* IDE integration<br />
** Improve build targets organization in IDE that support folders.<br />
** VisualStudio: Do not build documentation when F7 is pressed.<br />
** Facilitate integration with python IDE allowing minimal 'slicer' module to be imported.<br />
<br />
* Build-system<br />
** Add support for Visual Studio 2013.<br />
** Update build system anticipating transition to modern CMake.<br />
** Update MacOSX packaging infrastructure anticipating the signing of package in future release.<br />
** Update code base anticipitating compliance with C++11.<br />
** Remove <code>Slicer_ITKV3_COMPATIBILITY</code> build option.<br />
** Add option <code>Slicer_USE_ITKPython</code> to turn on ITK Python wrapping.<br />
** Removed duplicated code using ITK version of MGHIO.<br />
<br />
* Python<br />
** Added support writing scripted modules and widgets as new-style Python classes. <br />
** Simplify scripted module introducing SlicerPythonCppAPI. <br />
** Introduced qSlicerScriptedUtils::executeFile() <br />
** Updated CTK to include new PythonQt C++/Python ownership tracking feature. <br />
** Import scripted module as python module to avoid module top-level variables to clobber each other. [http://www.na-mic.org/Bug/view.php?id=3549 #3549]<br />
<br />
* Platform support<br />
** Fix support for 32-bit build allowing use of Slicer on Surface tablet.<br />
<br />
* Packaging & Testing infrastructure<br />
** Capturing VTK errors/warnings during testing.<br />
** Update extension build system to report packaging error on CDash.<br />
<br />
* Rendering / Visualization<br />
** Add Slicer_VTK_RENDERING_BACKEND configure option<br />
<br />
* MRML<br />
** Add support in vtkMRMLNode for multiple references to the same node. <br />
** Introducing InvokeCustomModifiedEvent. <br />
<br />
* ApplicationLogic<br />
** Added function PropagateLabelVolumeSelection(), PropagateForegroundVolumeSelection() and PropagateBackgroundVolumeSelection()<br />
<br />
* VolumeRenderingLogic<br />
** Extend volume rendering logic API adding GetPresetByName function. <br />
<br />
* View management<br />
** Add qMRMLLayoutViewFactory <br />
** Add utility method to display node in only 1 view <br />
<br />
* DICOM<br />
** [https://github.com/Slicer/Slicer/pull/359 upgraded DCMTK to the latest snapshot DCMTK-3.6.1_20150924]<br />
--><br />
<br />
<gallery caption="Improved Toolkits" widths="350px" heights="250px" perrow="3"><br />
<br />
Image:CTK-Logo.png|Moved from CTK [https://github.com/commontk/CTK/commit/f64b68a f64b68a] to [https://github.com/commontk/CTK/commit/1c97e54 1c97e54] (499 commits) <!-- git log --oneline f64b68acd717dab060db41e8bee3f0f30df1a58f...1c97e5426f898bc7d074e6122992d0dd12bab56b --no-merges | wc -l --><br />
<br />
Image:CTKApplauncher_Logo.png|Moved from CTKAppLauncher v0.1.11 to v0.1.14 (43 commits) <!-- git log --oneline v0.1.11..v0.1.14 --no-merges | wc -l --><br />
<br />
Image:ITK_logo.png|Moved from ITK v4.4.1 to v4.6.0 (1089 commits) <!-- git log --oneline v4.4.1..56fae27 --no-merges | wc -l --><br />
<br />
Image:OpenIGTLink-Logo.png|Moved from OpenIGTLink [https://github.com/openigtlink/OpenIGTLink/compare/66e272d...849b434 66e272d to 849b434] (53 commits) <!-- git log --no-merges --oneline 66e272d..849b434 | wc -l --><br />
<br />
Image:Qt-logo.png |Moved from Qt 4.7.4 to Qt 4.8.6<br />
<br />
Image:VTK_logo.png|Moved from VTK v5.10.1 to VTK v6.2.0 (5490 commits) <!-- git log --oneline v5.10.1..b55dad7 --no-merges | wc -l --><br />
<br />
Image:DCMTK_logo.png|[https://github.com/Slicer/Slicer/pull/359 Upgraded DCMTK to DCMTK-3.6.1_20150924 snapshot]<br />
<br />
</gallery><br />
<br />
== Looking at the Code Changes ==<br />
<br />
From a git checkout you can easily see the all the commits since the time of the 4.4.0 release:<br />
<br />
git log v4.4.0..HEAD<br />
<br />
To see a summary of your own commits, you could use something like:<br />
<br />
git log v4.4.0..HEAD --oneline --author=me<br />
<br />
see [https://www.kernel.org/pub/software/scm/git/docs/git-log.html the git log man page] for more options.<br />
<br />
[[Release_Details#Slicer_{{documentation/version}}.0|Commit stats and full changelog]]<br />
<br />
= Related Projects =<br />
<gallery caption="" widths="250px" heights="150px" perrow="3"><br />
</gallery></div>Laurenhttps://www.slicer.org/w/index.php?title=File:DMRI_3D_SLICER-icon.png&diff=47246File:DMRI 3D SLICER-icon.png2016-10-11T18:14:24Z<p>Lauren: </p>
<hr />
<div></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=47192Documentation/Nightly/Modules/UKFTractography2016-10-08T15:29:00Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
For additional references, please see below (References section).<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: <br />
** Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. [http://www.ncbi.nlm.nih.gov/pubmed/20805043 Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675].<br />
* Reference for 1-tensor and 2-tensor + free-water: <br />
** Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. [http://cmic.cs.ucl.ac.uk/cdmri12/pdfs/o1_3.pdf A unified tractography framework for comparing diffusion models on clinical scans. In Computational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).]<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: <br />
** Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. [http://www.ncbi.nlm.nih.gov/pubmed/26082890 Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22].<br />
* Reference for NODDI UKF tractography:<br />
** Reddy, C.P. and Rathi, Y., 2016. [http://www.ncbi.nlm.nih.gov/pubmed/27147956 Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter. Frontiers in Neuroscience, 10].<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/pnlbwh/ukftractography<br />
* https://www.nitrc.org/projects/ukftractography<br />
<br />
<!-- ---------------------------- --><br />
<!-- {{documentation/{{documentation/version}}/module-footer}} --><br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements&diff=47088Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements2016-09-15T21:13:12Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:DiffusionTensorScalarMeasurements screenshot Trace.png|Diffusion Tensor Trace<br />
|Image:DiffusionTensorScalarMeasurements screenshot FA.png|Diffusion Tensor FA<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Create FA (fractional anisotropy image) <br />
* Use Case 2: Quantify FA or another measure in a region of interest (using also the Editor and Quantification->Label Statistics)<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
* Slicer4 Diffusion Tensor Imaging Tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
*Trace: trace of the diffusion tensor (equal to the sum of its eigenvalues). Trace = 3 * MD, where MD is mean diffusivity.<br />
*Determinant: determinant of diffusion tensor.<br />
*RelativeAnisotropy: ratio of the anisotropic part of the diffusion tensor to the isotropic part (Basser, 1994)<br />
*FractionalAnisotropy: the degree of anisotropy of diffusion (Between 0 and 1 where 0 = completely isotropic, 1 = completely anisotropic). This measure can be thought of as quantifying how far the shape of diffusion is from a sphere. <br />
*Mode: Quantifies how linear or planar the diffusion is (cigar vs. pancake shape). FA and mode are orthogonal measures. (Ennis & Kindlmann, 2006).<br />
*LinearMeasure: is high in "cigar-shape" tensors, where the first eigenvalue is much larger than the others (Westin, 2002).<br />
*PlanarMeasure: is high in "pancake-shape" planar tensors, where the smallest eigenvalue is much less than the others (Westin, 2002).<br />
*SphericalMeasure: is high where all three eigenvalues are equal, giving a "sphere-shape" of the tensor (Westin, 2002).<br />
*MinEigenvalue: the smallest of the three eigenvalues of the diffusion tensor (also called lambda 3). <br />
*MidEigenvalue: the second smallest eigenvalue of the diffusion tensor (also called lambda 2).<br />
*MaxEigenvalue: the largest of the three eigenvalues of the diffusion tensor (also called lambda 1).<br />
*ParallelDiffusivity: this is equal to the first (maximum) eigenvalue, and also known as lambda 1, axial diffusivity (AD), and longitudinal diffusion coefficient. Note this is equal to MaxEigenvalue, above.<br />
*PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3. This is also known as radial diffusivity (RD) and transverse diffusion coefficient.<br />
<br />
Please note that the large number of names for the maximum eigenvalue is due to historical reasons. The original mathematical names are maximum eigenvalue or lambda 1. The names parallel, axial, and longitudinal diffusivity were introduced to help interpret the measure in terms of diffusivity along a hypothesized single fiber tract in the voxel.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* DWIToDTIEstimation<br />
* FiberTractMeasurements<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/8130344 Basser, 1994]<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/12044998 Westin, 2002]<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/16342267 Ennis & Kindlmann, 2006]<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements&diff=47079Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements2016-09-14T21:18:01Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:DiffusionTensorScalarMeasurements screenshot Trace.png|Diffusion Tensor Trace<br />
|Image:DiffusionTensorScalarMeasurements screenshot FA.png|Diffusion Tensor FA<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Create FA (fractional anisotropy image) <br />
* Use Case 2: Quantify FA or another measure in a region of interest (using also the Editor and Quantification->Label Statistics)<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
* Slicer4 Diffusion Tensor Imaging Tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
*Trace: trace of the diffusion tensor (equal to the sum of its eigenvalues). Trace = 3 * MD, where MD is mean diffusivity.<br />
*Determinant: determinant of diffusion tensor.<br />
*RelativeAnisotropy: ratio of the anisotropic part of the diffusion tensor to the isotropic part (Basser, 1994)<br />
*FractionalAnisotropy: the degree of anisotropy of diffusion (Between 0 and 1 where 0 = completely isotropic, 1 = completely anisotropic). This measure can be thought of as quantifying how far the shape of diffusion is from a sphere. <br />
*Mode: Quantifies how linear or planar the diffusion is (cigar vs. pancake shape). FA and mode are orthogonal measures. (Ennis & Kindlmann, 2006).<br />
*LinearMeasure: is high in "cigar-shape" tensors, where the first eigenvalue is much larger than the others (Westin, 2002).<br />
*PlanarMeasure: is high in "pancake-shape" planar tensors, where the smallest eigenvalue is much less than the others (Westin, 2002).<br />
*SphericalMeasure: is high where all three eigenvalues are equal, giving a "sphere-shape" of the tensor (Westin, 2002).<br />
*MinEigenvalue: the smallest of the three eigenvalues of the diffusion tensor (also called lambda 3). <br />
*MidEigenvalue: the second smallest eigenvalue of the diffusion tensor (also called lambda 2).<br />
*MaxEigenvalue: the largest of the three eigenvalues of the diffusion tensor (also called lambda 1).<br />
*ParallelDiffusivity: this is equal to the first (maximum) eigenvalue, and also known as lambda 1 and axial diffusivity (AD). So this is equal to MaxEigenvalue, above.<br />
*PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3. This is also known as radial diffusivity (RD).<br />
<br />
Please note that the large number of names for the maximum eigenvalue is due to historical reasons. The original mathematical names are maximum eigenvalue or lambda 1. The names parallel and axial diffusivity were introduced to help interpret the measure in terms of diffusivity along a hypothesized single fiber tract in the voxel.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* DWIToDTIEstimation<br />
* FiberTractMeasurements<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/8130344 Basser, 1994]<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/12044998 Westin, 2002]<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/16342267 Ennis & Kindlmann, 2006]<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements&diff=47078Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements2016-09-14T21:12:05Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
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<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:DiffusionTensorScalarMeasurements screenshot Trace.png|Diffusion Tensor Trace<br />
|Image:DiffusionTensorScalarMeasurements screenshot FA.png|Diffusion Tensor FA<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Create FA (fractional anisotropy image) <br />
* Use Case 2: Quantify FA or another measure in a region of interest (using also the Editor and Quantification->Label Statistics)<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
* Slicer4 Diffusion Tensor Imaging Tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
*Trace: trace of the diffusion tensor (equal to the sum of its eigenvalues). Trace = 3 * MD, where MD is mean diffusivity.<br />
*Determinant: determinant of diffusion tensor.<br />
*RelativeAnisotropy: ratio of the anisotropic part of the diffusion tensor to the isotropic part (Basser, 1994)<br />
*FractionalAnisotropy: the degree of anisotropy of diffusion (Between 0 and 1 where 0 = completely isotropic, 1 = completely anisotropic). This measure can be thought of as quantifying how far the shape of diffusion is from a sphere. <br />
*Mode: Quantifies how linear or planar the diffusion is (cigar vs. pancake shape). FA and mode are orthogonal measures. (Ennis & Kindlmann, 2006).<br />
*LinearMeasure: is high in "cigar-shape" tensors, where the first eigenvalue is much larger than the others (Westin, 2002).<br />
*PlanarMeasure: is high in "pancake-shape" planar tensors, where the smallest eigenvalue is much less than the others (Westin, 2002).<br />
*SphericalMeasure: is high where all three eigenvalues are equal, giving a "sphere-shape" of the tensor (Westin, 2002).<br />
*MinEigenvalue: the smallest of the three eigenvalues of the diffusion tensor (also called lambda 3). <br />
*MidEigenvalue: the second smallest eigenvalue of the diffusion tensor (also called lambda 2).<br />
*MaxEigenvalue: the largest of the three eigenvalues of the diffusion tensor (also called lambda 1).<br />
*ParallelDiffusivity: this is equal to the first (maximum) eigenvalue, and also known as lambda 1 and axial diffusivity (AD). So this is equal to MaxEigenvalue, above.<br />
*PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3. This is also known as radial diffusivity (RD).<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* DWIToDTIEstimation<br />
* FiberTractMeasurements<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/8130344 Basser, 1994]<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/12044998 Westin, 2002]<br />
* [http://www.ncbi.nlm.nih.gov/pubmed/16342267 Ennis & Kindlmann, 2006]<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMSave&diff=47051Documentation/Nightly/Modules/TractographyDICOMSave2016-09-13T18:51:45Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Save DICOM format tractography files in 3D Slicer.<br />
* Use Case 2: Convert vtk format tractography files to DICOM format on the command line.<br />
<br />
Both use cases require a reference diffusion-weighted MRI DICOM scan. The reference scan must be the DICOM data from which the tractography was created.<br />
<br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* TractographyDICOMLoad: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMLoad<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
* The CLI module is called: VTK_to_DICOMTract<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMSave&diff=47050Documentation/Nightly/Modules/TractographyDICOMSave2016-09-13T18:48:39Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Save DICOM format tractography files in 3D Slicer.<br />
* Use Case 2: Convert vtk format tractography files to DICOM format on the command line.<br />
<br />
Both use cases require a reference diffusion MRI DICOM scan. The reference scan must be the DICOM data from which the tractography was created.<br />
<br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* TractographyDICOMLoad: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMLoad<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
* The CLI module is called: VTK_to_DICOMTract<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMLoad&diff=47049Documentation/Nightly/Modules/TractographyDICOMLoad2016-09-13T18:41:22Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Load DICOM format tractography files into 3D Slicer.<br />
<br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* TractographyDICOMSave: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMSave<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
* The CLI module is called: DICOMTract_to_VTK<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMSave&diff=47048Documentation/Nightly/Modules/TractographyDICOMSave2016-09-13T18:40:43Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
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<br />
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{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Load DICOM format tractography files into 3D Slicer.<br />
<br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* TractographyDICOMLoad: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMLoad<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
* The CLI module is called: VTK_to_DICOMTract<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/ModulesMetadata&diff=47047Documentation/Nightly/ModulesMetadata2016-09-13T18:28:41Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<includeonly><br />
Name, XMLDescriptionURL<br />
ACPCTransform, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ACPCTransform/ACPCTransform.xml?revision=22697&view=co<br />
AddScalarVolumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/AddScalarVolumes/AddScalarVolumes.xml?revision=19194&view=co<br />
AffineRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/AffineRegistration/AffineRegistration.xml?revision=19194&view=co<br />
Annotations, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Annotations/Documentation/Annotations.xml?view=co&pathrev=25061<br />
BRAINSDemonWarp, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSDemonWarp/BRAINSDemonWarp.xml<br />
BRAINSFit, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSFit/BRAINSFit.xml<br />
BRAINSResample, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSResample/BRAINSResample.xml<br />
BSplineDeformableRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/BSplineDeformableRegistration/BSplineDeformableRegistration.xml?revision=19194&view=co<br />
BSplineToDeformationField, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/BSplineToDeformationField/BSplineToDeformationField.xml?revision=19194&view=co<br />
Cameras, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Core/Cameras/Documentation/Cameras.xml?revision=19238&view=co<br />
CastScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CastScalarVolume/CastScalarVolume.xml?revision=19608&view=co<br />
CheckerBoardFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CheckerBoardFilter/CheckerBoardFilter.xml?revision=19170&view=co<br />
CLIModuleTemplate, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Extensions/Testing/CLIExtensionTemplate/CLIModuleTemplate/CLIModuleTemplate.xml?revision=22715&view=co<br />
CreateDICOMSeries, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CreateDICOMSeries/CreateDICOMSeries.xml?revision=19171&view=co<br />
CurvatureAnisotropicDiffusion, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CurvatureAnisotropicDiffusion/CurvatureAnisotropicDiffusion.xml?revision=18864&view=co<br />
Data, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Data/Documentation/Data.xml?view=co&pathrev=25061<br />
DICOM, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Scripted/Scripts/DICOM.xml?revision=19903&view=co<br />
DicomToNrrdConverter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DicomToNrrdConverter/DicomToNrrdConverter.xml?revision=19089&view=co<br />
DiffusionTensorScalarMeasurements, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/DiffusionTensorScalarMeasurements/DiffusionTensorScalarMeasurements.xml<br />
DiffusionWeightedVolumeMasking, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/DiffusionWeightedVolumeMasking/DiffusionWeightedVolumeMasking.xml<br />
DTIExport, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DTIImportExport/DTIexport.xml?revision=19928&view=co<br />
DTIImport, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DTIImportExport/DTIimport.xml?revision=19928&view=co<br />
DWICompare, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DicomToNrrdConverter/ExtendedTesting/DWICompare.xml?revision=18864&view=co<br />
DWIConverter, https://raw.githubusercontent.com/BRAINSia/BRAINSTools/487208d64667a95ece1a4e4a6aa1f19273108d18/DWIConvert/DWIConvert.xml<br />
DWModeling, https://raw.githubusercontent.com/SlicerProstate/SlicerProstate/master/DWModeling/DWModeling.xml<br />
DWIJointRicianLMMSEFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DWIJointRicianLMMSEFilter/DWIJointRicianLMMSEFilter.xml?revision=19197&view=co<br />
DWIRicianLMMSEFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DWIRicianLMMSEFilter/DWIRicianLMMSEFilter.xml?revision=19197&view=co<br />
DWIToDTIEstimation, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/DWIToDTIEstimation/DWIToDTIEstimation.xml<br />
DWIUnbiasedNonLocalMeansFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DWIUnbiasedNonLocalMeansFilter/DWIUnbiasedNonLocalMeansFilter.xml?revision=19197&view=co<br />
EncodeSEG, https://raw.githubusercontent.com/QIICR/Reporting/master/SEGSupport/EncodeSEG.xml<br />
EMSegment_Command-line, http://viewvc.slicer.org/viewvc.cgi/Slicer3/trunk/Modules/EMSegment/CommandLineApplication/EMSegmentCommandLine.xml?revision=16924&view=co<br />
Endoscopy, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Scripted/Scripts/Endoscopy.xml?revision=18864&view=co<br />
EventBroker, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Core/EventBroker/Documentation/EventBroker.xml?revision=19045&view=co<br />
ExecutionModelTour, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ExecutionModelTour/ExecutionModelTour.xml?revision=19194&view=co<br />
ExpertAutomatedRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ExpertAutomatedRegistration/ExpertAutomatedRegistration.xml?revision=19173&view=co<br />
ExtractSkeleton, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ExtractSkeleton/ExtractSkeleton.xml?revision=18864&view=co<br />
FiberBundleLabelSelect, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/FiberBundleLabelSelect/FiberBundleLabelSelect.xml<br />
FiberTractMeasurements, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/FiberTractMeasurements/FiberTractMeasurements.xml<br />
FiducialRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/FiducialRegistration/FiducialRegistration.xml?revision=19173&view=co<br />
ForegroundMasking, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSROIAuto/BRAINSROIAuto.xml<br />
FreesurferSurfaceSectionExtraction, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/FreesurferSurfaceSectionExtraction/FreesurferSurfaceSectionExtraction.xml?revision=18864&view=co<br />
GaussianBlurImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GaussianBlurImageFilter/GaussianBlurImageFilter.xml?revision=19194&view=co<br />
GradientAnisotropicDiffusion, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GradientAnisotropicDiffusion/GradientAnisotropicDiffusion.xml?revision=18864&view=co<br />
GrayscaleFillHoleImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GrayscaleFillHoleImageFilter/GrayscaleFillHoleImageFilter.xml?revision=19194&view=co<br />
GrayscaleGrindPeakImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GrayscaleGrindPeakImageFilter/GrayscaleGrindPeakImageFilter.xml?revision=19194&view=co<br />
GrayscaleModelMaker, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GrayscaleModelMaker/GrayscaleModelMaker.xml?revision=18864&view=co<br />
HistogramMatching, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/HistogramMatching/HistogramMatching.xml?revision=18864&view=co<br />
ImageLabelCombine, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ImageLabelCombine/ImageLabelCombine.xml?revision=18864&view=co<br />
LabelMapSmoothing, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/LabelMapSmoothing/LabelMapSmoothing.xml?revision=18864&view=co<br />
LinearRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/LinearRegistration/LinearRegistration.xml?revision=19194&view=co<br />
Markups, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Markups/Documentation/Markups.xml?view=co&pathrev=25061<br />
MaskScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MaskScalarVolume/MaskScalarVolume.xml?revision=19608&view=co<br />
MatlabCommander, https://www.assembla.com/code/slicerrt/subversion/node/blob/trunk/MatlabBridge/src/MatlabCommander/MatlabCommander.xml?raw=1&rev=957<br />
MedianImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MedianImageFilter/MedianImageFilter.xml?revision=19194&view=co<br />
MergeModels, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MergeModels/MergeModels.xml?revision=22697&view=co<br />
MeshContourSegmentation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MeshContourSegmentation/MeshContourSegmentation.xml?revision=19175&view=co<br />
ModelMaker, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ModelMaker/ModelMaker.xml?revision=22697&view=co<br />
Models, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Models/Documentation/Models.xml?revision=25018&view=co<br />
ModelToLabelMap, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ModelToLabelMap/ModelToLabelMap.xml?revision=22697&view=co<br />
MRIBiasFieldCorrection, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MRIBiasFieldCorrection/MRIBiasFieldCorrection.xml?revision=18864&view=co<br />
MultiplyScalarVolumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MultiplyScalarVolumes/MultiplyScalarVolumes.xml?revision=19194&view=co<br />
MultiResolutionAffineRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MultiResolutionAffineRegistration/MultiResolutionAffineRegistration.xml?revision=19194&view=co<br />
N4ITKBiasFieldCorrection, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/N4ITKBiasFieldCorrection/N4ITKBiasFieldCorrection.xml?revision=22688&view=co<br />
OrientScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/OrientScalarVolume/OrientScalarVolume.xml?revision=19193&view=co<br />
OtsuThresholdImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/OtsuThresholdImageFilter/OtsuThresholdImageFilter.xml?revision=19194&view=co<br />
OtsuThresholdSegmentation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/OtsuThresholdSegmentation/OtsuThresholdSegmentation.xml?revision=18864&view=co<br />
PETStandardUptakeValueComputation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/PETStandardUptakeValueComputation/PETStandardUptakeValueComputation.xml?revision=19608&view=co<br />
PkModeling, https://raw.githubusercontent.com/millerjv/PkModeling/master/CLI/PkModeling.xml<br />
ProbeVolumeWithModel, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ProbeVolumeWithModel/ProbeVolumeWithModel.xml?revision=19194&view=co<br />
QuadEdgeSurfaceMesher, https://raw.githubusercontent.com/SlicerProstate/SlicerProstate/master/QuadEdgeSurfaceMesher/QuadEdgeSurfaceMesher.xml<br />
Reformat, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Reformat/Documentation/Reformat.xml?revision=19165&view=co<br />
ResampleDTIVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ResampleDTIVolume/ResampleDTIVolume.xml?revision=19197&view=co<br />
ResampleScalarVectorDWIVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ResampleScalarVectorDWIVolume/ResampleScalarVectorDWIVolume.xml?revision=19186&view=co<br />
ResampleScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ResampleScalarVolume/ResampleScalarVolume.xml?revision=19185&view=co<br />
RigidRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/RigidRegistration/RigidRegistration.xml?revision=19194&view=co<br />
RobustStatisticsSegmenter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/RobustStatisticsSegmenter/RobustStatisticsSegmenter.xml?revision=19198&view=co<br />
SceneViews, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/SceneViews/Documentation/SceneViews.xml?revision=19608&view=co<br />
SEG2NRRD, https://raw.githubusercontent.com/QIICR/Reporting/master/SEGSupport/SEG2NRRD.xml<br />
SegmentationSmoothing, https://raw.githubusercontent.com/SlicerProstate/SlicerProstate/master/SegmentationSmoothing/SegmentationSmoothing.xml<br />
SimpleRegionGrowingSegmentation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/SimpleRegionGrowingSegmentation/SimpleRegionGrowingSegmentation.xml?revision=22482&view=co<br />
SubtractScalarVolumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/SubtractScalarVolumes/SubtractScalarVolumes.xml?revision=19194&view=co<br />
Tables, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Tables/Documentation/Tables.xml?revision=24790&view=co<br />
ThresholdScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ThresholdScalarVolume/ThresholdScalarVolume.xml?revision=22407&view=co<br />
TractographyDICOMLoad, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/Loadable/TractIO/TractIOCLI/DICOMTract_to_VTK.xml<br />
TractographyDICOMSave, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/Loadable/TractIO/TractIOCLI/VTK_to_DICOMTract.xml<br />
TractographyLabelMapSeeding, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/TractographyLabelMapSeeding/TractographyLabelMapSeeding.xml<br />
Transforms, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Transforms/Documentation/Transforms.xml?view=co&pathrev=25061<br />
UKFTractography, https://raw.githubusercontent.com/pnlbwh/ukftractography/master/ukf/UKFTractography.xml<br />
ViewControllers, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/ViewControllers/Documentation/ViewControllers.xml?revision=18864&view=co<br />
VolumeRendering, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/VolumeRendering/Documentation/VolumeRendering.xml?view=co&pathrev=25061<br />
Volumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Volumes/Documentation/Volumes.xml?view=co&pathrev=25061<br />
VotingBinaryHoleFillingImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/VotingBinaryHoleFillingImageFilter/VotingBinaryHoleFillingImageFilter.xml?revision=19194&view=co<br />
</includeonly><noinclude><br />
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</noinclude></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMSave&diff=47046Documentation/Nightly/Modules/TractographyDICOMSave2016-09-13T17:13:45Z<p>Lauren: Created page with "<noinclude>{{documentation/versioncheck}}</noinclude> <!-- ---------------------------- --> {{documentation/{{documentation/version}}/module-header}} <!-- --------------------..."</p>
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Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
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{{documentation/{{documentation/version}}/module-introduction-row}}<br />
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|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
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Most frequently used for these scenarios:<br />
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* Use Case 1: Load DICOM format tractography files into 3D Slicer.<br />
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* TractographyDICOMSave: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMSave<br />
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* http://slicerdmri.github.io/<br />
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* https://github.com/SlicerDMRI<br />
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<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMLoad&diff=47045Documentation/Nightly/Modules/TractographyDICOMLoad2016-09-13T17:13:08Z<p>Lauren: </p>
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Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
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{{documentation/{{documentation/version}}/module-description}}<br />
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{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Load DICOM format tractography files into 3D Slicer.<br />
<br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
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* TractographyDICOMSave: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMSave<br />
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{{documentation/{{documentation/version}}/module-section|References}}<br />
* http://slicerdmri.github.io/<br />
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* https://github.com/SlicerDMRI<br />
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{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/TractographyDICOMLoad&diff=47044Documentation/Nightly/Modules/TractographyDICOMLoad2016-09-13T17:11:58Z<p>Lauren: Created page with "<noinclude>{{documentation/versioncheck}}</noinclude> <!-- ---------------------------- --> {{documentation/{{documentation/version}}/module-header}} <!-- --------------------..."</p>
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{{documentation/{{documentation/version}}/module-introduction-row}}<br />
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{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
}}<br />
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{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
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{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
Most frequently used for these scenarios:<br />
<br />
* Use Case 1: Load DICOM format tractography files into 3D Slicer.<br />
<br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
Links to tutorials that use this module<br />
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* TractographyInteractiveSeeding: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/TractographyDICOMSave<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/SlicerDMRI<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46781Documentation/Nightly/Modules/UKFTractography2016-08-05T18:57:13Z<p>Lauren: </p>
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<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
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{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: <br />
** Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675.<br />
* Reference for 1-tensor and 2-tensor + free-water: <br />
** Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. A unified tractography framework for comparing diffusion models on clinical scans. In Computational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: <br />
** Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22.<br />
* Reference for NODDI UKF tractography:<br />
** Reddy, C.P. and Rathi, Y., 2016. Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter. Frontiers in neuroscience, 10.<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/pnlbwh/ukftractography<br />
* https://www.nitrc.org/projects/ukftractography<br />
<br />
<!-- ---------------------------- --><br />
<!-- {{documentation/{{documentation/version}}/module-footer}} --><br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46780Documentation/Nightly/Modules/UKFTractography2016-08-05T18:55:17Z<p>Lauren: </p>
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<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: <br />
** Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675.<br />
* Reference for 1-tensor and 2-tensor + free-water: <br />
** Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. A unified tractography framework for comparing diffusion models on clinical scans. In Computational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: <br />
** Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22.<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/pnlbwh/ukftractography<br />
* https://www.nitrc.org/projects/ukftractography<br />
<br />
<!-- ---------------------------- --><br />
<!-- {{documentation/{{documentation/version}}/module-footer}} --><br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46779Documentation/Nightly/Modules/UKFTractography2016-08-05T18:53:53Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675.<br />
* Reference for 1-tensor and 2-tensor + free-water: Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. A unified tractography framework for comparing diffusion models on clinical scans. InComputational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22.<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/pnlbwh/ukftractography<br />
* https://www.nitrc.org/projects/ukftractography<br />
<br />
<!-- ---------------------------- --><br />
<!-- {{documentation/{{documentation/version}}/module-footer}} --><br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DWIConverter&diff=46776Documentation/Nightly/Modules/DWIConverter2016-08-05T18:42:45Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Vince Magnotta (UIowa)<br><br />
Contributor1: Hans Johnson (UIowa)<br><br />
Contributor2: Joy Matsui (UIowa)<br><br />
Contributor3: Kent Williams (UIowa)<br><br />
Contributor4: Mark Scully (Uiowa)<br><br />
Contributor5: Xiaodong Tao (GE)<br><br />
Contact: Kent Williams, <email>norman-k-williams@uiowa.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:Dwiconverter 1.png|DWIConverter inputs<br />
|Image:Dwiconverter 2.png|DWIConverter advanced parameters<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* Loading DICOM diffusion MRI data into Slicer. <br />
* Conversion of diffusion weighted images (DWIs) from DICOM format to nrrd or nifti formats.<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* Tutorial: http://slicerdmri.github.io/docs/tutorials/DWIConverterTutorial.pdf<br />
* Test data: http://slicer.kitware.com/midas3/download/item/93008/SiemensTrioTim2.tar.gz<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* http://slicerdmri.github.io/<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
N/A<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
<!-- {{documentation/{{documentation/version}}/module-developerinfo}} --><br />
* https://github.com/BRAINSia/BRAINSTools<br />
* http://wiki.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:DICOM_for_DWI_and_DTI<br />
<br />
<br />
<!-- ---------------------------- --><br />
<!-- {{documentation/{{documentation/version}}/module-footer}} --><br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DWIConverter&diff=46775Documentation/Nightly/Modules/DWIConverter2016-08-05T18:39:37Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Vince Magnotta (UIowa)<br><br />
Contributor1: Hans Johnson (UIowa)<br><br />
Contributor2: Joy Matsui (UIowa)<br><br />
Contributor3: Kent Williams (UIowa)<br><br />
Contributor4: Mark Scully (Uiowa)<br><br />
Contributor5: Xiaodong Tao (GE)<br><br />
Contact: Kent Williams, <email>norman-k-williams@uiowa.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:Dwiconverter 1.png|DWIConverter inputs<br />
|Image:Dwiconverter 2.png|DWIConverter advanced parameters<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* Loading DICOM diffusion MRI data into Slicer. <br />
* Conversion of diffusion weighted images (DWIs) from DICOM format to nrrd or nifti formats.<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* Tutorial: http://slicerdmri.github.io/docs/tutorials/DWIConverterTutorial.pdf<br />
* Test data: http://slicer.kitware.com/midas3/download/item/93008/SiemensTrioTim2.tar.gz<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
N/A<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
N/A<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
{{documentation/{{documentation/version}}/module-developerinfo}}<br />
* https://github.com/BRAINSia/BRAINSTools<br />
* http://wiki.na-mic.org/Wiki/index.php/NAMIC_Wiki:DTI:DICOM_for_DWI_and_DTI<br />
<br />
<br />
<!-- ---------------------------- --><br />
<!-- {{documentation/{{documentation/version}}/module-footer}} --><br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46774Documentation/Nightly/Modules/UKFTractography2016-08-05T18:33:05Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675.<br />
* Reference for 1-tensor and 2-tensor + free-water: Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. A unified tractography framework for comparing diffusion models on clinical scans. InComputational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22.<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/pnlbwh/ukftractography<br />
<br />
<!-- ---------------------------- --><br />
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<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46773Documentation/Nightly/Modules/UKFTractography2016-08-05T18:32:16Z<p>Lauren: </p>
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Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
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{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
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{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
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* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
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{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675.<br />
* Reference for 1-tensor and 2-tensor + free-water: Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. A unified tractography framework for comparing diffusion models on clinical scans. InComputational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22.<br />
<br />
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{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
* https://github.com/pnlbwh/ukftractography<br />
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{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46772Documentation/Nightly/Modules/UKFTractography2016-08-05T18:31:02Z<p>Lauren: </p>
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{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
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{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
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{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
* Tractography Seeding<br />
* Tractography ROI Seeding<br />
* Diffusion Tensor Estimation<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
* Reference for 2-tensor tractography: Malcolm, J.G., Shenton, M.E. and Rathi, Y., 2010. Filtered multitensor tractography. IEEE transactions on medical imaging, 29(9), pp.1664-1675.<br />
* Reference for 1-tensor and 2-tensor + free-water: Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin C, Rathi Y. A unified tractography framework for comparing diffusion models on clinical scans. InComputational Diffusion MRI Workshop of MICCAI, Nice 2012 (pp. 27-32).<br />
* Reference for using UKF in clinical imaging data from tumor patients with edema: Chen Z, Tie Y, Olubiyi O, Rigolo L, Mehrtash A, Norton I, Pasternak O, Rathi Y, Golby AJ, O'Donnell LJ. Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical. 2015 Dec 31;7:815-22.<br />
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{{documentation/{{documentation/version}}/module-developerinfo}}<br />
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{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46771Documentation/Nightly/Modules/UKFTractography2016-08-05T18:23:29Z<p>Lauren: </p>
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{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
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{{documentation/{{documentation/version}}/module-description}}<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
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{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
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Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
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--><br />
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N/A<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
<br />
<br />
<br />
<br />
<br />
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{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
{{documentation/{{documentation/version}}/module-developerinfo}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46770Documentation/Nightly/Modules/UKFTractography2016-08-05T18:22:23Z<p>Lauren: </p>
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<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
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{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The default tensor model is a cylinder: both smaller eigenvalues are equal. The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
<br />
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N/A<br />
<br />
<br />
<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
{{documentation/{{documentation/version}}/module-developerinfo}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46769Documentation/Nightly/Modules/UKFTractography2016-08-05T18:20:52Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
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<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:UKF 1.png|Arcuate fasciculus (AF) tract in the setting of edema [Chen et al, 2015]<br />
|Image:UKF 2.png|Corpus callosum (CC) tract<br />
}}<br />
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{{documentation/{{documentation/version}}/module-introduction-end}}<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
By default, this module uses a tensor model (either one or two tensors). The NODDI model can also be used.<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
<br />
<!--<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
--><br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
N/A<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
{{documentation/{{documentation/version}}/module-developerinfo}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46766Documentation/Nightly/Modules/UKFTractography2016-08-05T17:58:51Z<p>Lauren: </p>
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{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:NAC-logo.png|NAC<br />
|Image:DiffusionTensorScalarMeasurements screenshot Trace.png|REPLACE<br />
|Image:DiffusionTensorScalarMeasurements screenshot FA.png|REPLACE<br />
}}<br />
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{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
It is very easy to expand the framework and to implement new fiber representations <br />
for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor <br />
methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion <br />
ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be <br />
identical) and the other one uses a full tensor representation.<br />
<br />
Authors: Yogesh Rathi (yogesh@bwh.harvard.edu), Stefan Lienhard, Yinpeng Li, Martin<br />
Styner, Ipek Oguz, Yundi Shi, Christian Baumgartner (c.f.baumgartner@gmail.com),<br />
Ryan Eckbo, Lauren O'Donnell, Jessica Lee <br />
<br />
<!--<br />
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If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:<pre>{{documentation/{{documentation/version}}/module-description}}<br />
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{{documentation/{{documentation/version}}/module-description}}<br />
--><br />
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<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
<br />
<br />
<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
<br />
<!--<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
--><br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
N/A<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
{{documentation/{{documentation/version}}/module-developerinfo}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46765Documentation/Nightly/Modules/UKFTractography2016-08-05T17:55:53Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br><br />
Website: http://slicerdmri.github.io/<br />
<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:DiffusionTensorScalarMeasurements screenshot Trace.png|REPLACE<br />
|Image:DiffusionTensorScalarMeasurements screenshot FA.png|REPLACE<br />
}}<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
<br />
<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
It is very easy to expand the framework and to implement new fiber representations <br />
for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor <br />
methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion <br />
ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be <br />
identical) and the other one uses a full tensor representation.<br />
<br />
Authors: Yogesh Rathi (yogesh@bwh.harvard.edu), Stefan Lienhard, Yinpeng Li, Martin<br />
Styner, Ipek Oguz, Yundi Shi, Christian Baumgartner (c.f.baumgartner@gmail.com),<br />
Ryan Eckbo, Lauren O'Donnell, Jessica Lee <br />
<br />
<!--<br />
Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br />
<br />
If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:<pre>{{documentation/{{documentation/version}}/module-description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
--><br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
<br />
<!--<br />
{{documentation/{{documentation/version}}/module-parametersdescription}}<br />
--><br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
N/A<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
{{documentation/{{documentation/version}}/module-developerinfo}}<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46760Documentation/Nightly/Modules/UKFTractography2016-08-05T17:51:07Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
<br />
{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
<br />
Contact: <email>slicer-users@bwh.harvard.edu</email><br><br />
Website: https://github.com/pnlbwh/ukftractography<br />
Website: http://slicerdmri.github.io/<br />
<br />
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Yogesh Rathi, Ph.D, Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital<br><br />
<br />
Contact: Ryan Eckbo, <email>reckbo@bwh.harvard.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|{{collaborator|logo|isomics}}|{{collaborator|longname|isomics}} <- Replace this logo with yours<br />
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}} <-Replace this logo with yours<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
It is very easy to expand the framework and to implement new fiber representations <br />
for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor <br />
methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion <br />
ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be <br />
identical) and the other one uses a full tensor representation.<br />
<br />
Authors: Yogesh Rathi (yogesh@bwh.harvard.edu), Stefan Lienhard, Yinpeng Li, Martin<br />
Styner, Ipek Oguz, Yundi Shi, Christian Baumgartner (c.f.baumgartner@gmail.com),<br />
Ryan Eckbo, Lauren O'Donnell, Jessica Lee <br />
<br />
<!--<br />
Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br />
<br />
If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:<pre>{{documentation/{{documentation/version}}/module-description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
--><br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
<br />
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{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
<br />
<br />
<br />
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<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/ModulesMetadata&diff=46759Documentation/Nightly/ModulesMetadata2016-08-05T17:48:56Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<includeonly><br />
Name, XMLDescriptionURL<br />
ACPCTransform, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ACPCTransform/ACPCTransform.xml?revision=22697&view=co<br />
AddScalarVolumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/AddScalarVolumes/AddScalarVolumes.xml?revision=19194&view=co<br />
AffineRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/AffineRegistration/AffineRegistration.xml?revision=19194&view=co<br />
Annotations, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Annotations/Documentation/Annotations.xml?view=co&pathrev=25061<br />
BRAINSDemonWarp, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSDemonWarp/BRAINSDemonWarp.xml<br />
BRAINSFit, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSFit/BRAINSFit.xml<br />
BRAINSResample, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSResample/BRAINSResample.xml<br />
BSplineDeformableRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/BSplineDeformableRegistration/BSplineDeformableRegistration.xml?revision=19194&view=co<br />
BSplineToDeformationField, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/BSplineToDeformationField/BSplineToDeformationField.xml?revision=19194&view=co<br />
Cameras, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Core/Cameras/Documentation/Cameras.xml?revision=19238&view=co<br />
CastScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CastScalarVolume/CastScalarVolume.xml?revision=19608&view=co<br />
CheckerBoardFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CheckerBoardFilter/CheckerBoardFilter.xml?revision=19170&view=co<br />
CLIModuleTemplate, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Extensions/Testing/CLIExtensionTemplate/CLIModuleTemplate/CLIModuleTemplate.xml?revision=22715&view=co<br />
CreateDICOMSeries, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CreateDICOMSeries/CreateDICOMSeries.xml?revision=19171&view=co<br />
CurvatureAnisotropicDiffusion, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/CurvatureAnisotropicDiffusion/CurvatureAnisotropicDiffusion.xml?revision=18864&view=co<br />
Data, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Data/Documentation/Data.xml?view=co&pathrev=25061<br />
DICOM, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Scripted/Scripts/DICOM.xml?revision=19903&view=co<br />
DicomToNrrdConverter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DicomToNrrdConverter/DicomToNrrdConverter.xml?revision=19089&view=co<br />
DiffusionTensorScalarMeasurements, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/DiffusionTensorScalarMeasurements/DiffusionTensorScalarMeasurements.xml<br />
DiffusionWeightedVolumeMasking, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/DiffusionWeightedVolumeMasking/DiffusionWeightedVolumeMasking.xml<br />
DTIExport, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DTIImportExport/DTIexport.xml?revision=19928&view=co<br />
DTIImport, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DTIImportExport/DTIimport.xml?revision=19928&view=co<br />
DWICompare, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DicomToNrrdConverter/ExtendedTesting/DWICompare.xml?revision=18864&view=co<br />
DWIConverter, https://raw.githubusercontent.com/BRAINSia/BRAINSTools/487208d64667a95ece1a4e4a6aa1f19273108d18/DWIConvert/DWIConvert.xml<br />
DWModeling, https://raw.githubusercontent.com/SlicerProstate/SlicerProstate/master/DWModeling/DWModeling.xml<br />
DWIJointRicianLMMSEFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DWIJointRicianLMMSEFilter/DWIJointRicianLMMSEFilter.xml?revision=19197&view=co<br />
DWIRicianLMMSEFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DWIRicianLMMSEFilter/DWIRicianLMMSEFilter.xml?revision=19197&view=co<br />
DWIToDTIEstimation, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/DWIToDTIEstimation/DWIToDTIEstimation.xml<br />
DWIUnbiasedNonLocalMeansFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/DWIUnbiasedNonLocalMeansFilter/DWIUnbiasedNonLocalMeansFilter.xml?revision=19197&view=co<br />
EncodeSEG, https://raw.githubusercontent.com/QIICR/Reporting/master/SEGSupport/EncodeSEG.xml<br />
EMSegment_Command-line, http://viewvc.slicer.org/viewvc.cgi/Slicer3/trunk/Modules/EMSegment/CommandLineApplication/EMSegmentCommandLine.xml?revision=16924&view=co<br />
Endoscopy, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Scripted/Scripts/Endoscopy.xml?revision=18864&view=co<br />
EventBroker, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Core/EventBroker/Documentation/EventBroker.xml?revision=19045&view=co<br />
ExecutionModelTour, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ExecutionModelTour/ExecutionModelTour.xml?revision=19194&view=co<br />
ExpertAutomatedRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ExpertAutomatedRegistration/ExpertAutomatedRegistration.xml?revision=19173&view=co<br />
ExtractSkeleton, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ExtractSkeleton/ExtractSkeleton.xml?revision=18864&view=co<br />
FiberBundleLabelSelect, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/FiberBundleLabelSelect/FiberBundleLabelSelect.xml<br />
FiberTractMeasurements, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/FiberTractMeasurements/FiberTractMeasurements.xml<br />
FiducialRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/FiducialRegistration/FiducialRegistration.xml?revision=19173&view=co<br />
ForegroundMasking, https://raw.github.com/BRAINSia/BRAINSTools/master/BRAINSROIAuto/BRAINSROIAuto.xml<br />
FreesurferSurfaceSectionExtraction, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/FreesurferSurfaceSectionExtraction/FreesurferSurfaceSectionExtraction.xml?revision=18864&view=co<br />
GaussianBlurImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GaussianBlurImageFilter/GaussianBlurImageFilter.xml?revision=19194&view=co<br />
GradientAnisotropicDiffusion, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GradientAnisotropicDiffusion/GradientAnisotropicDiffusion.xml?revision=18864&view=co<br />
GrayscaleFillHoleImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GrayscaleFillHoleImageFilter/GrayscaleFillHoleImageFilter.xml?revision=19194&view=co<br />
GrayscaleGrindPeakImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GrayscaleGrindPeakImageFilter/GrayscaleGrindPeakImageFilter.xml?revision=19194&view=co<br />
GrayscaleModelMaker, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/GrayscaleModelMaker/GrayscaleModelMaker.xml?revision=18864&view=co<br />
HistogramMatching, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/HistogramMatching/HistogramMatching.xml?revision=18864&view=co<br />
ImageLabelCombine, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ImageLabelCombine/ImageLabelCombine.xml?revision=18864&view=co<br />
LabelMapSmoothing, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/LabelMapSmoothing/LabelMapSmoothing.xml?revision=18864&view=co<br />
LinearRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/LinearRegistration/LinearRegistration.xml?revision=19194&view=co<br />
Markups, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Markups/Documentation/Markups.xml?view=co&pathrev=25061<br />
MaskScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MaskScalarVolume/MaskScalarVolume.xml?revision=19608&view=co<br />
MatlabCommander, https://www.assembla.com/code/slicerrt/subversion/node/blob/trunk/MatlabBridge/src/MatlabCommander/MatlabCommander.xml?raw=1&rev=957<br />
MedianImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MedianImageFilter/MedianImageFilter.xml?revision=19194&view=co<br />
MergeModels, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MergeModels/MergeModels.xml?revision=22697&view=co<br />
MeshContourSegmentation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MeshContourSegmentation/MeshContourSegmentation.xml?revision=19175&view=co<br />
ModelMaker, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ModelMaker/ModelMaker.xml?revision=22697&view=co<br />
Models, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Models/Documentation/Models.xml?revision=25018&view=co<br />
ModelToLabelMap, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ModelToLabelMap/ModelToLabelMap.xml?revision=22697&view=co<br />
MRIBiasFieldCorrection, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MRIBiasFieldCorrection/MRIBiasFieldCorrection.xml?revision=18864&view=co<br />
MultiplyScalarVolumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MultiplyScalarVolumes/MultiplyScalarVolumes.xml?revision=19194&view=co<br />
MultiResolutionAffineRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/MultiResolutionAffineRegistration/MultiResolutionAffineRegistration.xml?revision=19194&view=co<br />
N4ITKBiasFieldCorrection, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/N4ITKBiasFieldCorrection/N4ITKBiasFieldCorrection.xml?revision=22688&view=co<br />
OrientScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/OrientScalarVolume/OrientScalarVolume.xml?revision=19193&view=co<br />
OtsuThresholdImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/OtsuThresholdImageFilter/OtsuThresholdImageFilter.xml?revision=19194&view=co<br />
OtsuThresholdSegmentation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/OtsuThresholdSegmentation/OtsuThresholdSegmentation.xml?revision=18864&view=co<br />
PETStandardUptakeValueComputation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/PETStandardUptakeValueComputation/PETStandardUptakeValueComputation.xml?revision=19608&view=co<br />
PkModeling, https://raw.githubusercontent.com/millerjv/PkModeling/master/CLI/PkModeling.xml<br />
ProbeVolumeWithModel, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ProbeVolumeWithModel/ProbeVolumeWithModel.xml?revision=19194&view=co<br />
QuadEdgeSurfaceMesher, https://raw.githubusercontent.com/SlicerProstate/SlicerProstate/master/QuadEdgeSurfaceMesher/QuadEdgeSurfaceMesher.xml<br />
Reformat, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Reformat/Documentation/Reformat.xml?revision=19165&view=co<br />
ResampleDTIVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ResampleDTIVolume/ResampleDTIVolume.xml?revision=19197&view=co<br />
ResampleScalarVectorDWIVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ResampleScalarVectorDWIVolume/ResampleScalarVectorDWIVolume.xml?revision=19186&view=co<br />
ResampleScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ResampleScalarVolume/ResampleScalarVolume.xml?revision=19185&view=co<br />
RigidRegistration, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/RigidRegistration/RigidRegistration.xml?revision=19194&view=co<br />
RobustStatisticsSegmenter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/RobustStatisticsSegmenter/RobustStatisticsSegmenter.xml?revision=19198&view=co<br />
SceneViews, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/SceneViews/Documentation/SceneViews.xml?revision=19608&view=co<br />
SEG2NRRD, https://raw.githubusercontent.com/QIICR/Reporting/master/SEGSupport/SEG2NRRD.xml<br />
SegmentationSmoothing, https://raw.githubusercontent.com/SlicerProstate/SlicerProstate/master/SegmentationSmoothing/SegmentationSmoothing.xml<br />
SimpleRegionGrowingSegmentation, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/SimpleRegionGrowingSegmentation/SimpleRegionGrowingSegmentation.xml?revision=22482&view=co<br />
SubtractScalarVolumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/SubtractScalarVolumes/SubtractScalarVolumes.xml?revision=19194&view=co<br />
Tables, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Tables/Documentation/Tables.xml?revision=24790&view=co<br />
ThresholdScalarVolume, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/ThresholdScalarVolume/ThresholdScalarVolume.xml?revision=22407&view=co<br />
TractographyLabelMapSeeding, https://raw.githubusercontent.com/SlicerDMRI/SlicerDMRI/master/Modules/CLI/TractographyLabelMapSeeding/TractographyLabelMapSeeding.xml<br />
Transforms, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Transforms/Documentation/Transforms.xml?view=co&pathrev=25061<br />
UKFTractography, https://raw.githubusercontent.com/pnlbwh/ukftractography/master/ukf/UKFTractography.xml<br />
ViewControllers, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/ViewControllers/Documentation/ViewControllers.xml?revision=18864&view=co<br />
VolumeRendering, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/VolumeRendering/Documentation/VolumeRendering.xml?view=co&pathrev=25061<br />
Volumes, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/Loadable/Volumes/Documentation/Volumes.xml?view=co&pathrev=25061<br />
VotingBinaryHoleFillingImageFilter, http://viewvc.slicer.org/viewvc.cgi/Slicer4/trunk/Modules/CLI/VotingBinaryHoleFillingImageFilter/VotingBinaryHoleFillingImageFilter.xml?revision=19194&view=co<br />
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</noinclude></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46758Documentation/Nightly/Modules/UKFTractography2016-08-05T17:47:17Z<p>Lauren: </p>
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{{documentation/{{documentation/version}}/module-acknowledgements}}<br />
<br />
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Yogesh Rathi, Ph.D, Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital<br><br />
<br />
Contact: Ryan Eckbo, <email>reckbo@bwh.harvard.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
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|{{collaborator|logo|isomics}}|{{collaborator|longname|isomics}} <- Replace this logo with yours<br />
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}} <-Replace this logo with yours<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
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{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
It is very easy to expand the framework and to implement new fiber representations <br />
for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor <br />
methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion <br />
ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be <br />
identical) and the other one uses a full tensor representation.<br />
<br />
Authors: Yogesh Rathi (yogesh@bwh.harvard.edu), Stefan Lienhard, Yinpeng Li, Martin<br />
Styner, Ipek Oguz, Yundi Shi, Christian Baumgartner (c.f.baumgartner@gmail.com),<br />
Ryan Eckbo, Lauren O'Donnell, Jessica Lee <br />
<br />
<!--<br />
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If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:<pre>{{documentation/{{documentation/version}}/module-description}}<br />
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{{documentation/{{documentation/version}}/module-description}}<br />
--><br />
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{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
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{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
<br />
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{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
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<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DWIConverter&diff=46757Documentation/Nightly/Modules/DWIConverter2016-08-05T17:41:50Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Vince Magnotta (UIowa)<br><br />
Contributor1: Hans Johnson (UIowa)<br><br />
Contributor2: Joy Matsui (UIowa)<br><br />
Contributor3: Kent Williams (UIowa)<br><br />
Contributor4: Mark Scully (Uiowa)<br><br />
Contributor5: Xiaodong Tao (GE)<br><br />
Contact: Kent Williams, <email>norman-k-williams@uiowa.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|{{collaborator|logo|isomics}}|{{collaborator|longname|isomics}} <- Replace this logo with yours<br />
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}} <-Replace this logo with yours<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
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{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
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{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* Loading DICOM diffusion MRI data into Slicer. <br />
* Conversion of diffusion weighted images (DWIs) from DICOM format to nrrd or nifti formats.<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* Tutorial: http://slicerdmri.github.io/docs/tutorials/DWIConverterTutorial.pdf<br />
* Test data: http://slicer.kitware.com/midas3/download/item/93008/SiemensTrioTim2.tar.gz<br />
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{{documentation/{{documentation/version}}/module-developerinfo}}<br />
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{{documentation/{{documentation/version}}/module-footer}}<br />
<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/UKFTractography&diff=46756Documentation/Nightly/Modules/UKFTractography2016-08-05T17:37:38Z<p>Lauren: </p>
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<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Yogesh Rathi, Ph.D, Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital<br><br />
<br />
Contact: Ryan Eckbo, <email>reckbo@bwh.harvard.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|{{collaborator|logo|isomics}}|{{collaborator|longname|isomics}} <- Replace this logo with yours<br />
|{{collaborator|logo|spl}}|{{collaborator|longname|spl}} <-Replace this logo with yours<br />
}}<br />
{{documentation/{{documentation/version}}/module-introduction-end}}<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
We present a framework which uses an unscented Kalman filter for performing<br />
tractography. At each point on the fiber the most consistent direction is found<br />
as a mixture of previous estimates and of the local model.<br />
<br />
It is very easy to expand the framework and to implement new fiber representations <br />
for it. Currently it is possible to tract fibers using two different 1-, 2-, or 3-tensor <br />
methods. Both methods use a mixture of Gaussian tensors. One limits the diffusion <br />
ellipsoids to a cylindrical shape (the second and third eigenvalue are assumed to be <br />
identical) and the other one uses a full tensor representation.<br />
<br />
Authors: Yogesh Rathi (yogesh@bwh.harvard.edu), Stefan Lienhard, Yinpeng Li, Martin<br />
Styner, Ipek Oguz, Yundi Shi, Christian Baumgartner (c.f.baumgartner@gmail.com),<br />
Ryan Eckbo, Lauren O'Donnell, Jessica Lee <br />
<br />
<!--<br />
Here comes a description what the module is good for. Explain briefly how it works and point to the [[documentation/{{documentation/version}}/Modules/{{documentation/modulename}}#References|references]] giving more details on the algorithm.<br />
<br />
If you are documenting a CLI, the description should be extracted from the corresponding XML description. This could be done automatically using the following wiki template:<pre>{{documentation/{{documentation/version}}/module-description}}<br />
<br />
{{documentation/{{documentation/version}}/module-description}}<br />
--><br />
<br />
<br />
<br />
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<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Use Cases}}<br />
* 1-Tensor tractography<br />
* 1-Tensor tractography with free water<br />
* 2-Tensor tractography<br />
* 2-Tensor tractography with free water<br />
* Neurite orientation dispersion and density imaging (NODDI)<br />
<br />
<br />
<br />
<br />
<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Tutorials}}<br />
* UKF Tractography tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/4.5/Training#UKF<br />
<br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
<br />
Input/Output (IO)<br />
<br />
--dwiFile <std::string> Input diffusion weighted (DWI) volume<br />
<br />
--seedsFile <std::string> Seeds for diffusion. If not specified, full brain tractography will be performed, and the algorithm will start from every voxel in the brain mask where the Generalized Anisotropy is bigger than 0.18<br />
<br />
--labels <std::vector<int>> A vector of the ROI labels to be used. These are the voxel values where tractography should be seeded. (default: 1)<br />
<br />
--maskFile <std::string> Brain mask for diffusion tractography. Tracking will only be performed inside this mask. <br />
<br />
--tracts <std::string> Output fiber tracts generated with one tensor.<br />
<br />
<br />
<br />
Tractography Options<br />
<br />
--seedsPerVoxel <int> Tractography parameter used in all models: number of seeds per voxel. Each seed generates a fiber, thus using more seeds generates more fibers. In general use 1 or 2 seeds, and for a more thorough result use 5 or 10 (depending on your machine this may take up to 2 days to run). Default: 1. Range: 0-50. (default: 1)<br />
<br />
--seedFALimit<double> Tractography parameter used in all models. Seed points whose fractional anisotropy (FA) are below this value are excluded. Default: 0.18. Range: 0-1.<br />
<br />
--minFA <double> Tractography parameter used in tensor model. Tractography will stop when the fractional anisotropy (FA) of the tensor being tracked is less than this value. Note: make sure to also decrease the GA to track through lower anisotropy areas. This parameter is used only in tensor models. Default: 0.15. Range: 0-1.<br />
<br />
--minGA <double> Tractography parameter used in all models. Tractography will stop when the generalized anisotropy (GA) is less than this value. GA is a normalized variance of the input signals (so it does not depend on any model). Note: to extend tracking through low anisotropy areas, this parameter is often more effective than the minFA. This parameter is used by both tensor and NODDI models. Default: 0.1. Range: 0-1. <br />
<br />
--numThreads <int> Tractography parameter used in all models. Number of threads used during computation. Set to the number of cores on your workstation for optimal speed. If left undefined, the number of cores detected will be used.<br />
<br />
--numTensor <1|2|3> Number of tensors (tensor model) or orientations (NODDI model) used. (default: 2)<br />
<br />
--stepLength <double> Tractography parameter used in all models. Step size when conducting tractography (in mm). Default: 0.3. Range: 0.1-1.<br />
<br />
--Qm <double> Rate of change of tensor direction/orientation. UKF data fitting parameter for tensor or NODDI model: Process noise for angles/direction. Defaults: Noddi-0.001; Single tensor-0.005; other-0.001. Suggested Range: 0.00001 - 0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--recordLength <double> Tractography parameter used in all models. Step size between points saved along fibers. Default: 0.9. Range: 0.1-4.<br />
<br />
--maxHalfFiberLength <double> Tractography parameter used in all models. The max length limit of the half fibers generated during tractography. A fiber is "half" when the tractography goes in only one direction from one seed point at a time. Default: 250 mm. Range: 1-500 mm.<br />
<br />
--recordNMSE Record output from data fitting: Store normalized mean square error (NMSE) along fibers. <br />
<br />
<br />
Tensor Model<br />
<br />
--freeWater Adds a term for free water diffusion to the model. The free water model is a tensor with all 3 eigenvalues equal to the diffusivity of free water (0.003). To output the free water fraction, make sure to use the "save free water" parameter.<br />
<br />
--recordFA Record output from tensor model: Save fractional anisotropy (FA) of the tensor(s). Attaches field 'FA' or 'FA1' and 'FA2' for 2-tensor case to fiber.<br />
<br />
--recordTrace Record output from tensor model: Save the trace of the tensor(s). Attaches field 'Trace' or 'Trace1' and 'Trace2' for 2-tensor case to fiber.<br />
<br />
--recordFreeWater Record output from tensor plus free water model: Save the fraction of free water. Attaches field 'FreeWater' to fiber.<br />
<br />
--recordTensors Record output from tensor model: Save the tensors that were computed during tractography (if using tensor model). The fields will be called 'TensorN', where N is the tensor number. Recording the tensors enables Slicer to color the fiber bundles by FA, orientation, and so on. Recording the tensors also enables quantitative analyses.<br />
<br />
--Ql <double> UKF data fitting parameter for tensor model: rate of change of eigenvalues. Defaults: 1 tensor-300 ; 2 tensor-50 ; 3 tensor-100. Suggested Range: 1-1000. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
--Qw <double> UKF data fitting parameter for tensor plus free water model: Process noise for free water weights, ignored if no free water estimation. Defaults: 1 tensor-0.0025; 2 tensor-0.0015. Suggested Range: 0.00001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
NODDI Model: <br />
<br />
--noddi Use neurite orientation dispersion and density imaging (NODDI) model instead of tensor model.<br />
<br />
--recordVic Save NODDI volume fraction of intra-cellular compartment along fibers. <br />
<br />
--recordKappa Save NODDI concentration parameter that measures the extent of orientation dispersion.<br />
<br />
--recordViso Save NODDI volume traction of CSF compartment along fibers.<br />
<br />
--Qkappa <double> UKF data fitting parameter for NODDI model: Rate of change of kappa value. Higher kappa values indicate more fiber dispersion. Default: 0.01.<br />
<br />
--Qvic <double> UKF data fitting parameter for NODDI model: Rate of change of volume fraction of intracellular component. Default: 0.004<br />
<br />
<br />
Signal Parameters (Expert Only)<br />
<br />
--Rs <double> Expected noise in signal. This is used by the UKF method to decide how much to trust the data. This should be increased for very noisy data or reduced for high quality data. Defaults: single tensor/orientation-0.01; other-0.02. Suggested Range: 0.001-0.25. Default of 0.0 indicates the program will assign value based on other model parameters.<br />
<br />
<br />
Debug/Develop Only<br />
<br />
--sigmaSignal <double> igma for Gaussian kernel used to interpolate the signal at sub-voxel locations. Default: 0.0<br />
<br />
--recordState Store the states along the fiber. Will generate field 'state'. The state is the model for UKF. In the case of the two tensor model, it is a ten-parameter vector.<br />
<br />
--recordCovariance Store the covariance matrix along the fiber. Will generate field 'covariance' in fiber. This is the covariance from the unscented Kalman filter.<br />
<br />
--fullTensorModel Use the full tensor model instead of the default model. The default model has both smaller eigenvalues equal, whereas the full model allows 3 different eigenvalues.<br />
<br />
--maxBranchingAngle <double> Maximum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Branching is supressed when this maxBranchingAngle is set to 0.0. Default: 0.0. Range: 0-90.<br />
<br />
--minBranchingAngle <double> Minimum branching angle, in degrees. When using multiple tensors, a new branch will be created when the tensors' major directions form an angle between (minBranchingAngle, maxBranchingAngle). Default: 0. Range: 0-90.<br />
<br />
--tractsWithSecondTensor <double> Tracts generated, with second tensor output (if there is one)<br />
<br />
--storeGlyphs Store tensors' main directions as two-point lines in a separate file named glyphs_{tracts}. When using multiple tensors, only the major tensors' main directions are stored<br />
<br />
<br />
Tractography Command Line Options<br />
<br />
--returnparameterfile <std::string> Filename in which to write simple return parameters (int, float, int-vector, etc.) as opposed to bulk return parameters (image, geometry, transform, measurement, table).<br />
<br />
--processinformationaddress <std::string> Address of a structure to store process information (progress, abort, etc.). (default: 0)<br />
<br />
--xml Produce xml description of command line arguments.<br />
<br />
--echo Echo the command line arguments.<br />
<br />
--, --ignore_rest Ignores the rest of the labeled arguments following this flag.<br />
<br />
--version Displays version information and exits.<br />
<br />
-h, --help Displays usage information and exits. <br />
<br />
<br />
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{{documentation/{{documentation/version}}/module-section|References}}<br />
Reference for 2-tensor tractography:<br />
* http://pnl.bwh.harvard.edu/pub/papers_html/MalcolmIEEETransMed10.html<br />
<br />
Reference for 1-tensor and 2-tensor + free-water:<br />
* C. Baumgartner, O. Michailovich, O. Pasternak, S. Bouix, J. Levitt, ME Shenton, C-F Westin, Y. Rathi,<br />
"A unified tractography framework for comparing diffusion models on clinical scans": in Workshop<br />
on computational diffusion MRI, 2012.<br />
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<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/DWIConverter&diff=46755Documentation/Nightly/Modules/DWIConverter2016-08-05T17:36:57Z<p>Lauren: </p>
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This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br><br />
Author: Vince Magnotta (UIowa)<br><br />
Contributor1: Hans Johnson (UIowa)<br><br />
Contributor2: Joy Matsui (UIowa)<br><br />
Contributor3: Kent Williams (UIowa)<br><br />
Contributor4: Mark Scully (Uiowa)<br><br />
Contributor5: Xiaodong Tao (GE)<br><br />
Contact: Kent Williams, <email>norman-k-williams@uiowa.edu</email><br><br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
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|{{collaborator|logo|isomics}}|{{collaborator|longname|isomics}} <- Replace this logo with yours<br />
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* Tutorial: http://slicerdmri.github.io/docs/tutorials/DWIConverterTutorial.pdf<br />
* Test data: http://slicer.kitware.com/midas3/download/item/93008/SiemensTrioTim2.tar.gz<br />
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<!-- ---------------------------- --></div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/2016-User-Manual-Discussion&diff=46575Documentation/Nightly/2016-User-Manual-Discussion2016-07-20T21:22:25Z<p>Lauren: </p>
<hr />
<div>__NOTOC__<br />
{| border="0" align="center" width="100%" valign="top" cellspacing="7" cellpadding="2"<br />
|-<br />
! width="50%"|<br />
! |<br />
! width="50%"|<br />
|- <br />
|valign="top"|<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">3D Slicer</span><br />
----<br />
* Analysis and visualization of medical images<br />
* Image guided therapy research, device interfaces.<br />
* Extensible, free, open source. See [https://www.slicer.org/pages/LicenseText here] for the license<br />
<br />
|bgcolor="#CCCCCC"|<br />
|valign="top"|<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">Highlights</span><br />
----<br />
* Designed for biomedical research<br />
* Multi organ capability: from head to toe.<br />
* Multi-modality imaging: MRI, CT, US, nuclear medicine, and microscopy.<br />
|}<br />
<br />
{| border="0" align="center" width="100%" valign="top" cellspacing="7" cellpadding="2"<br />
|-<br />
! width="33%"|<br />
! |<br />
! width="33%"|<br />
! |<br />
! width="33%"|<br />
|- <br />
|valign="top"|<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">Where to start ?</span><br />
----<br />
* [[ New_users|Getting started]]<br />
* [[ Documentation/4.5/Training|Self-training]]<br />
* [[FAQ|Frequently asked questions]]<br />
* [http://massmail.bwh.harvard.edu/mailman/listinfo/slicer-users Mailing list for users of 3D Slicer] / [http://massmail.bwh.harvard.edu/mailman/listinfo/slicer-users Sign-up] <br> [http://slicer-users.65878.n3.nabble.com/ Search the mailing list archives]<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.5/Report_a_problem Report a problem or request a feature (edit that page accordingly, aimed at end users, not developers)]<br />
* How to [http://wiki.slicer.org/slicerWiki/index.php/CitingSlicer Cite Slicer] and Cite Slicer Modules<br />
----<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">The Basics</span><br />
----<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.5/SlicerApplication/Installation Installation]<br />
* A [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.5/SlicerApplication/MainApplicationGUI tour through the main window of 3D Slicer]<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/SlicerApplication/MainApplicationGUI#Layouts Layouts], [http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/SlicerApplication/MainApplicationGUI#3D_Viewer 3D Viewers], and [http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/SlicerApplication/MainApplicationGUI#Slice_Viewers Slice Viewers]<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.5/SlicerApplication/MouseandKeyboardShortcuts Mouse Buttons, "Hot-keys" and Keyboard Shortcuts]<br />
* Where to modify [[settings]] and where they are [[stored]] (refers to the Edit/Appplication Settings pop up panel)<br />
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.5/SlicerApplication/HardwareConfiguration Hardware requirements]<br />
* [[Link to portal page about loading data|Loading]], [[Saving data]] and [[listing of supported data formats]].<br />
<br />
<br />
|bgcolor="#CCCCCC"|<br />
|valign="top"|<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">Core Functions</span><br />
----<br />
<br />
{| class="wikitable"<br />
| <br />
| Data IO and Organization<br />
|-<br />
| [[Image:2016-07-03-Interactive-Editor-Icon.png|60 px| link=Link to Editor module |Interactive segmentation]] <br />
| Interactive and Automated Segmentation<br />
|-<br />
| [[Image:2016-07-03-Transforms-Module-Icon.png|60 px| Link= Link to Transforms module |Interactive]]<br />
| Interactive and Automated Registration<br />
|-<br />
| [[Image:SlicerRegistrationLibrary_Ad.png|80px|link=Documentation/Nightly/Registration/RegistrationLibrary]]<br />
| Registration library<br />
|-<br />
| [[Image:2016-07-03-Volume-Rendering-Icon.png|60 px| Link= Link to Volume rendering module | Volume rendering]] <br />
| Volume rendering<br />
|-<br />
| [[Link to the fiducials page | Fiducials]] and [[link to the linear measurements page| Linear Measurements]]<br />
| Measurements and Quantification<br />
|}<br />
<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">Advanced Topics</span><br />
----<br />
* [[Link to the proper page | Interactive tractography of diffusion MRI data]] <br />
* [[Link to the proper page | Slicer IGT]] <br />
* [[Link to the proper page | Slicer RT]] <br />
<br />
|bgcolor="#CCCCCC"|<br />
|valign="top"|<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">Miscellaneous</span><br />
----<br />
* [[Documentation/Nightly/Developers | Information for Software Developers]]<br />
* [[Slicer4:VisualBlog|Visual blog: A set of screenshots showing Slicer in action.]]<br />
* [[Documentation/{{documentation/version}}/ReleaseNotes|Release Notes: Platform specific issues and considerations]]<br />
* [[Documentation/{{documentation/version}}/Announcements|Announcements]] & [http://www.slicer.org/pages/Acknowledgments#Acknowledgments_Slicer_4 Acknowledgments]<br />
* Slicer Lookup Tables<br />
* Setting up and using stereoscopic viewing<br />
* QtTesting - Easy way to record and play macros<br />
<br />
<br />
<br />
----<br />
<span style="color: #555555; font-size: 18px; font-weight: bold;">Documentation in other languages </span><br />
----<br />
* [[Link to the appropriate language page | Español]]<br />
<br />
|}<br />
* [[search box right here|Search for a module in this version of Slicer (including extensions)]]<br />
* Listing in two columns:<br />
** Alphabetic list of modules<br />
** Alphabetic list of extensions</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/ScriptRepository&diff=46523Documentation/Nightly/ScriptRepository2016-07-14T14:40:33Z<p>Lauren: </p>
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<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
__TOC__<br />
<br />
<br />
=Community-contributed modules=<br />
<br />
Usage: save the .py file to a directory, add the directory to the additional module paths in the Slicer application settings (choose in the menu: Edit / Application settings, click Modules, click >> next to Additional module paths, click Add, and choose the .py file's location).<br />
<br />
==Filters==<br />
* [https://raw.github.com/pieper/VolumeMasker/master/VolumeMasker.py VolumeMasker.py]: Update a target volume with the results of setting all input volume voxels to 0 except for those that correspond to a selected label value in an input label map (Used for example in the volume rendering in [https://www.youtube.com/watch?v=dfu2gugHLHs this video).<br />
<br />
==DICOM==<br />
* [https://gist.github.com/pieper/6186477 dicom header browser] to easily scroll through dicom files using dcmdump.<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/src/BatchProcessing SlicerRT batch processing] to batch convert RT structure sets to labelmap NRRD files.<br />
<br />
==Informatics==<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/sandbox/MarkupsInfoModule/MarkupsInfo.py MarkupsInfo.py]: Compute the total length between all the points of a markup list.<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/sandbox/LineProfile/LineProfile.py LineProfile.py]: Compute intensity profile in a volume along a line.<br />
<br />
=Community-contributed examples=<br />
<br />
Usage: Copy-paste the shown code lines or linked .py file contents into Python console in Slicer.<br />
<br />
==Capture==<br />
* Get a MRML node in the scene based on the node name and call methods of that object. For the MRHead sample data:<br />
vol=slicer.util.getNode('MR*')<br />
vol.GetImageData().GetDimensions()<br />
* Capture the full Slicer screen and save it into a file<br />
img = qt.QPixmap.grabWidget(slicer.util.mainWindow()).toImage()<br />
img.save('c:/tmp/test.png')<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/sandbox/CaptureRotationVideo/CaptureRotationVideo.py CaptureRotationVideo.py]: Capture a video of the scene rotating in the 3D view<br />
<br />
==Launching Slicer==<br />
* How to open an .mrb file with Slicer at the command line?<br />
Slicer.exe --python-code "slicer.util.loadScene( 'f:/2013-08-23-Scene.mrb' )"<br />
* How to run a script in the Slicer environment in batch mode (without showing any graphical user interface)?<br />
Slicer.exe --python-code "doSomething; doSomethingElse; etc." --testing --no-splash --no-main-window<br />
<br />
==DICOM==<br />
* How to access tags of DICOM images imported into Slicer? For example, to print the first patient's first study's first series' "0020,0032" field:<br />
db=slicer.dicomDatabase<br />
patientList=db.patients()<br />
studyList=db.studiesForPatient(patientList[0])<br />
seriesList=db.seriesForStudy(studyList[0])<br />
fileList=db.filesForSeries(seriesList[0])<br />
print db.fileValue(fileList[0],'0020,0032')<br />
<br />
* How to access tag of a volume loaded from DICOM? For example, get the patient position stored in a volume:<br />
volumeName='2: ENT IMRT'<br />
n=slicer.util.getNode(volumeName)<br />
instUids=n.GetAttribute('DICOM.instanceUIDs').split()<br />
filename=slicer.dicomDatabase.fileForInstance(instUids[0])<br />
print slicer.dicomDatabase.fileValue(filename,'0018,5100')<br />
<br />
* How to access tag of an item in the Subject Hierachy tree? For example, get the content time tag of a structure set:<br />
rtStructName = '3: RTSTRUCT: PROS'<br />
rtStructNode = slicer.util.getNode(rtStructName)<br />
rtStructSubjectHierarchyNode = slicer.vtkMRMLSubjectHierarchyNode.GetAssociatedSubjectHierarchyNode(rtStructNode)<br />
ctSliceInstanceUids = rtStructSubjectHierarchyNode.GetAttribute('DICOM.ReferencedInstanceUIDs').split()<br />
filename = slicer.dicomDatabase.fileForInstance(ctSliceInstanceUids[0])<br />
print slicer.dicomDatabase.fileValue(filename,'0008,0033')<br />
<br />
==Toolbar functions==<br />
* How to turn on slice intersections in the crosshair menu on the toolbar:<br />
viewNodes = slicer.mrmlScene.GetNodesByClass('vtkMRMLSliceCompositeNode')<br />
viewNodes.UnRegister(slicer.mrmlScene)<br />
viewNodes.InitTraversal()<br />
viewNode = viewNodes.GetNextItemAsObject()<br />
while viewNode:<br />
viewNode.SetSliceIntersectionVisibility(1)<br />
viewNode = viewNodes.GetNextItemAsObject()<br />
<br />
How to find similar functions? For this one I searched for "slice intersections" text in the whole slicer source code, found that the function is implemented in Base\QTGUI\qSlicerViewersToolBar.cxx, then translated the qSlicerViewersToolBarPrivate::setSliceIntersectionVisible(bool visible) method to Python.<br />
<br />
==Manipulating objects in the slice viewer==<br />
* How to define/edit a circular region of interest in a slice viewer?<br />
<br />
Drop two markup points on a slice view and copy-paste the code below into the Python console. After this, as you move the markups you’ll see a circle following the markups.<br />
<br />
# Update the sphere from the fiducial points<br />
def UpdateSphere(param1, param2): <br />
centerPointCoord = [0.0, 0.0, 0.0]<br />
markups.GetNthFiducialPosition(0,centerPointCoord)<br />
circumferencePointCoord = [0.0, 0.0, 0.0]<br />
markups.GetNthFiducialPosition(1,circumferencePointCoord)<br />
sphere.SetCenter(centerPointCoord)<br />
radius=math.sqrt((centerPointCoord[0]-circumferencePointCoord[0])**2+(centerPointCoord[1]-circumferencePointCoord[1])**2+(centerPointCoord[2]-circumferencePointCoord[2])**2)<br />
sphere.SetRadius(radius)<br />
sphere.SetPhiResolution(30)<br />
sphere.SetThetaResolution(30)<br />
sphere.Update()<br />
<br />
# Get a reference to the markup<br />
markups=slicer.util.getNode('F')<br />
# Create the sphere that will intersect the slice viewer<br />
sphere = vtk.vtkSphereSource()<br />
# Initial positioning of the sphere<br />
UpdateSphere(0,0)<br />
# Create model node and add to scene<br />
model = slicer.vtkMRMLModelNode()<br />
model.SetAndObservePolyData(sphere.GetOutput())<br />
modelDisplay = slicer.vtkMRMLModelDisplayNode()<br />
modelDisplay.SetSliceIntersectionVisibility(True) # Show in slice view<br />
modelDisplay.SetVisibility(False) # Hide in 3D view<br />
slicer.mrmlScene.AddNode(modelDisplay)<br />
model.SetAndObserveDisplayNodeID(modelDisplay.GetID())<br />
modelDisplay.SetInputPolyData(model.GetPolyData())<br />
slicer.mrmlScene.AddNode(model) <br />
# Call UpdateSphere whenever the fiducials are changed<br />
markups.AddObserver("ModifiedEvent", UpdateSphere, 2)<br />
<br />
== Add a texture mapped plane to the scene as a model ==<br />
Note that model textures are not exposed in the GUI and are not saved in the scene<br />
<pre><br />
# use dummy image data here<br />
e = vtk.vtkImageEllipsoidSource()<br />
<br />
scene = slicer.mrmlScene<br />
<br />
# Create model node<br />
model = slicer.vtkMRMLModelNode()<br />
model.SetScene(scene)<br />
model.SetName(scene.GenerateUniqueName("2DImageModel"))<br />
<br />
planeSource = vtk.vtkPlaneSource()<br />
model.SetAndObservePolyData(planeSource.GetOutput())<br />
<br />
# Create display node<br />
modelDisplay = slicer.vtkMRMLModelDisplayNode()<br />
modelDisplay.SetColor(1,1,0) # yellow<br />
modelDisplay.SetBackfaceCulling(0)<br />
modelDisplay.SetScene(scene)<br />
scene.AddNode(modelDisplay)<br />
model.SetAndObserveDisplayNodeID(modelDisplay.GetID())<br />
<br />
# Add to scene<br />
modelDisplay.SetAndObserveTextureImageData(e.GetOutput())<br />
scene.AddNode(model) <br />
<br />
<br />
transform = slicer.vtkMRMLLinearTransformNode()<br />
scene.AddNode(transform) <br />
model.SetAndObserveTransformNodeID(transform.GetID())<br />
<br />
vTransform = vtk.vtkTransform()<br />
vTransform.Scale(50,50,50)<br />
vTransform.RotateX(30)<br />
transform.SetAndObserveMatrixTransformToParent(vTransform.GetMatrix())<br />
</pre><br />
<br />
<br />
== Export a model to Blender, including color ==<br />
<br />
<pre><br />
plyFilePath = "/tmp/fibers.ply"<br />
<br />
lineDisplayNode = getNode("*LineDisplay*")<br />
<br />
tuber = vtk.vtkTubeFilter()<br />
tuber.SetInput(lineDisplayNode.GetOutputPolyData())<br />
<br />
tubes = tuber.GetOutput()<br />
tubes.Update()<br />
scalars = tubes.GetPointData().GetArray(0)<br />
scalars.SetName("scalars")<br />
<br />
triangles = vtk.vtkTriangleFilter()<br />
triangles.SetInput(tubes)<br />
<br />
colorNode = lineDisplayNode.GetColorNode()<br />
lookupTable = vtk.vtkLookupTable()<br />
lookupTable.DeepCopy(colorNode.GetLookupTable())<br />
lookupTable.SetTableRange(0,1)<br />
<br />
plyWriter = vtk.vtkPLYWriter()<br />
plyWriter.SetInput(triangles.GetOutput())<br />
plyWriter.SetLookupTable(lookupTable)<br />
plyWriter.SetArrayName("scalars")<br />
<br />
plyWriter.SetFileName(plyFilePath)<br />
plyWriter.Write()<br />
</pre><br />
<br />
== Clone a volume ==<br />
This example shows how to clone the MRHead sample volume, including its pixel data and display settings.<br />
<pre><br />
sourceVolumeNode = slicer.util.getNode('MRHead')<br />
volumesLogic = slicer.modules.volumes.logic()<br />
clonedVolumeNode = volumesLogic.CloneVolume(slicer.mrmlScene, sourceVolumeNode, 'Cloned volume')<br />
</pre><br />
<br />
== Create a new volume ==<br />
This example shows how to create a new empty volume.<br />
<pre><br />
imageSize=[512, 512, 512]<br />
imageSpacing=[1.0, 1.0, 1.0]<br />
voxelType=vtk.VTK_UNSIGNED_CHAR<br />
# Create an empty image volume<br />
imageData=vtk.vtkImageData()<br />
imageData.SetDimensions(imageSize)<br />
imageData.AllocateScalars(voxelType, 1)<br />
thresholder=vtk.vtkImageThreshold()<br />
thresholder.SetInputData(imageData)<br />
thresholder.SetInValue(0)<br />
thresholder.SetOutValue(0)<br />
# Create volume node<br />
volumeNode=slicer.vtkMRMLScalarVolumeNode()<br />
volumeNode.SetSpacing(imageSpacing)<br />
volumeNode.SetImageDataConnection(thresholder.GetOutputPort())<br />
# Add volume to scene<br />
slicer.mrmlScene.AddNode(volumeNode)<br />
displayNode=slicer.vtkMRMLScalarVolumeDisplayNode()<br />
slicer.mrmlScene.AddNode(displayNode)<br />
colorNode = slicer.util.getNode('Grey')<br />
displayNode.SetAndObserveColorNodeID(colorNode.GetID())<br />
volumeNode.SetAndObserveDisplayNodeID(displayNode.GetID())<br />
volumeNode.CreateDefaultStorageNode()<br />
</pre><br />
<br />
== Modify voxels in a volume ==<br />
This example shows how to change voxels values of the MRHead sample volume.<br />
The values will be computed by function f(r,a,s,) = (r-10)*(r-10)+(a+15)*(a+15)+s*s.<br />
<pre><br />
volumeNode=slicer.util.getNode('MRHead')<br />
ijkToRas = vtk.vtkMatrix4x4()<br />
volumeNode.GetIJKToRASMatrix(ijkToRas)<br />
imageData=volumeNode.GetImageData()<br />
extent = imageData.GetExtent()<br />
for k in xrange(extent[4], extent[5]+1):<br />
for j in xrange(extent[2], extent[3]+1):<br />
for i in xrange(extent[0], extent[1]+1):<br />
position_Ijk=[i, j, k, 1]<br />
position_Ras=ijkToRas.MultiplyPoint(position_Ijk)<br />
r=position_Ras[0]<br />
a=position_Ras[1]<br />
s=position_Ras[2] <br />
functionValue=(r-10)*(r-10)+(a+15)*(a+15)+s*s<br />
imageData.SetScalarComponentFromDouble(i,j,k,0,functionValue)<br />
imageData.SetScalarComponentFromFloat(distortionVectorPosition_Ijk[0], distortionVectorPosition_Ijk[1], distortionVectorPosition_Ijk[2], 0, fillValue)<br />
imageData.Modified()<br />
</pre><br />
<br />
== Access values in a DTI tensor volume ==<br />
This example shows how to access individual tensors at the voxel level.<br />
<br />
First load your DWI volume and estimate tensors to produce a DTI volume called ‘Output DTI Volume’<br />
<br />
Then open the python window: View->Python interactor<br />
<br />
Use this command to access tensors through numpy:<br />
<br />
<pre><br />
tensors = array('Output DTI Volume')<br />
</pre><br />
<br />
Type the following code into the Python window to access all tensor components using vtk commands:<br />
<br />
<pre><br />
volumeNode=slicer.util.getNode('Output DTI Volume')<br />
imageData=volumeNode.GetImageData()<br />
tensors = imageData.GetPointData().GetTensors()<br />
extent = imageData.GetExtent()<br />
idx = 0<br />
for k in xrange(extent[4], extent[5]+1):<br />
for j in xrange(extent[2], extent[3]+1):<br />
for i in xrange(extent[0], extent[1]+1):<br />
tensors.GetTuple9(idx)<br />
idx += 1<br />
</pre><br />
<br />
== Change window/level (brightness/contrast) or colormap of a volume ==<br />
This example shows how to change window/level of the MRHead sample volume.<br />
<pre><br />
volumeNode = getNode('MRHead')<br />
displayNode = volumeNode.GetDisplayNode()<br />
displayNode.AutoWindowLevelOff()<br />
displayNode.SetWindow(50)<br />
displayNode.SetLevel(100)<br />
</pre><br />
<br />
Change color mapping from grayscale to rainbow:<br />
<pre><br />
displayNode.SetAndObserveColorNodeID('vtkMRMLColorTableNodeRainbow')<br />
</pre><br />
<br />
== Manipulate a Slice View ==<br />
<br />
<pre><br />
lm = slicer.app.layoutManager()<br />
red = lm.sliceWidget('Red')<br />
redLogic = red.sliceLogic()<br />
# Print current slice offset position<br />
print redLogic.GetSliceOffset()<br />
# Change slice position<br />
redLogic.SetSliceOffset(20)<br />
</pre><br />
<br />
== Save a series of images from a Slice View ==<br />
<br />
Save the following into a file such as '/tmp/record.py' and then in the slicer python console type "execfile('/tmp/record.py')"<br />
<br />
<pre><br />
layoutName = 'Green'<br />
imagePathPattern = '/tmp/image-%03d.png'<br />
steps = 10<br />
<br />
widget = slicer.app.layoutManager().sliceWidget(layoutName)<br />
view = widget.sliceView()<br />
logic = widget.sliceLogic()<br />
bounds = [0,]*6<br />
logic.GetSliceBounds(bounds)<br />
<br />
for step in range(steps):<br />
offset = bounds[4] + step/(1.*steps) * (bounds[5]-bounds[4])<br />
logic.SetSliceOffset(offset)<br />
view.forceRender()<br />
image = qt.QPixmap.grabWidget(view).toImage()<br />
image.save(imagePathPattern % step)<br />
</pre><br />
<br />
== Show a volume in the Slice Views ==<br />
<br />
<pre><br />
volumeNode = slicer.util.getNode('YourVolumeNode')<br />
applicationLogic = slicer.app.applicationLogic()<br />
selectionNode = applicationLogic.GetSelectionNode()<br />
selectionNode.SetSecondaryVolumeID(volumeNode.GetID())<br />
applicationLogic.PropagateForegroundVolumeSelection(0) <br />
</pre><br />
<br />
or<br />
<br />
<pre><br />
n = slicer.util.getNode('YourVolumeNode')<br />
for color in ['Red', 'Yellow', 'Green']:<br />
slicer.app.layoutManager().sliceWidget(color).sliceLogic().GetSliceCompositeNode().SetForegroundVolumeID(n.GetID())<br />
</pre><br />
<br />
== Center the 3D View on the Scene ==<br />
<pre><br />
layoutManager = slicer.app.layoutManager()<br />
threeDWidget = layoutManager.threeDWidget(0)<br />
threeDView = threeDWidget.threeDView()<br />
threeDView.resetFocalPoint()<br />
</pre><br />
<br />
== Display text in a 3D view or slice view ==<br />
<br />
The easiest way to show information overlaid on a viewer is to use corner annotations.<br />
<br />
<pre><br />
view=slicer.app.layoutManager().threeDWidget(0).threeDView()<br />
# Set text to "Something"<br />
view.cornerAnnotation().SetText(vtk.vtkCornerAnnotation.UpperRight,"Something")<br />
# Set color to red<br />
view.cornerAnnotation().GetTextProperty().SetColor(1,0,0)<br />
# Update the view<br />
view.forceRender()<br />
</pre><br />
<br />
== Turning off interpolation ==<br />
<br />
You can turn off interpolation for newly loaded volumes with this script from Steve Pieper.<br />
<br />
<pre><br />
def NoInterpolate(caller,event):<br />
for node in slicer.util.getNodes('*').values():<br />
if node.IsA('vtkMRMLScalarVolumeDisplayNode'):<br />
node.SetInterpolate(0)<br />
<br />
slicer.mrmlScene.AddObserver(slicer.mrmlScene.NodeAddedEvent, NoInterpolate)<br />
</pre><br />
<br />
The below link explains how to put this in your startup script.<br />
<br />
http://www.na-mic.org/Wiki/index.php/AHM2012-Slicer-Python#Refining_the_code_and_UI_with_slicerrc<br />
<br />
<br />
== Customize viewer layout ==<br />
<br />
Show a custom layout of a 3D view on top of the red slice view:<br />
<br />
<pre><br />
customLayout = ("<layout type=\"vertical\" split=\"true\" >"<br />
" <item>"<br />
" <view class=\"vtkMRMLViewNode\" singletontag=\"1\">"<br />
" <property name=\"viewlabel\" action=\"default\">1</property>"<br />
" </view>"<br />
" </item>"<br />
" <item>"<br />
" <view class=\"vtkMRMLSliceNode\" singletontag=\"Red\">"<br />
" <property name=\"orientation\" action=\"default\">Axial</property>"<br />
" <property name=\"viewlabel\" action=\"default\">R</property>"<br />
" <property name=\"viewcolor\" action=\"default\">#F34A33</property>"<br />
" </view>"<br />
" </item>"<br />
"</layout>")<br />
<br />
customLayoutId=501<br />
<br />
layoutManager = slicer.app.layoutManager()<br />
layoutManager.layoutLogic().GetLayoutNode().AddLayoutDescription(customLayoutId, customLayout) <br />
layoutManager.setLayout(customLayoutId)<br />
</pre><br />
<br />
See description of standard layouts (that can be used as examples) here:<br />
https://github.com/Slicer/Slicer/blob/master/Libs/MRML/Logic/vtkMRMLLayoutLogic.cxx<br />
<br />
== Running an ITK filter in Python using SimpleITK ==<br />
Open the "Sample Data" module and download "MR Head", then paste the following snippet in Python interactor:<br />
<pre><br />
inputImage = sitkUtils.PullFromSlicer('MRHead')<br />
filter = sitk.SignedMaurerDistanceMapImageFilter()<br />
outputImage = filter.Execute(inputImage)<br />
sitkUtils.PushToSlicer(outputImage,'outputImage')<br />
</pre><br />
<br />
More information:<br />
* See the SimpleITK documentation for SimpleITK examples: http://www.itk.org/SimpleITKDoxygen/html/examples.html<br />
* sitkUtils in Slicer is used for pushing and pulling images from Slicer to SimpleITK: https://github.com/Slicer/Slicer/blob/master/Base/Python/sitkUtils.py<br />
<br />
== Get current mouse coordinates in a slice view ==<br />
<br />
You can get 3D (RAS) coordinates of the current mouse cursor from the crosshair singleton node as shown in the example below:<br />
<br />
<pre><br />
def onMouseMoved(observer,eventid): <br />
ras=[0,0,0]<br />
crosshairNode.GetCursorPositionRAS(ras)<br />
print(ras)<br />
<br />
crosshairNode=slicer.util.getNode('Crosshair') <br />
crosshairNode.AddObserver(slicer.vtkMRMLCrosshairNode.CursorPositionModifiedEvent, onMouseMoved)<br />
</pre><br />
<br />
== Thick slab reconstruction and maximum/minimum intensity volume projections ==<br />
<br />
Set up 'red' slice viewer to show thick slab reconstructed from 3 slices:<br />
<pre><br />
sliceNode = slicer.mrmlScene.GetNodeByID('vtkMRMLSliceNodeRed')<br />
appLogic = slicer.app.applicationLogic()<br />
sliceLogic = appLogic.GetSliceLogic(sliceNode)<br />
sliceLayerLogic = sliceLogic.GetBackgroundLayer()<br />
reslice = sliceLayerLogic.GetReslice()<br />
reslice.SetSlabModeToMean()<br />
reslice.SetSlabNumberOfSlices(10) # mean of 10 slices will computed<br />
reslice.SetSlabSliceSpacingFraction(0.3) # spacing between each slice is 0.3 pixel (total 10 * 0.3 = 3 pixel neighborhood)<br />
sliceNode.Modified()<br />
</pre><br />
<br />
Set up 'red' slice viewer to show maximum intensity projection (MIP):<br />
<pre><br />
sliceNode = slicer.mrmlScene.GetNodeByID('vtkMRMLSliceNodeRed')<br />
appLogic = slicer.app.applicationLogic()<br />
sliceLogic = appLogic.GetSliceLogic(sliceNode)<br />
sliceLayerLogic = sliceLogic.GetBackgroundLayer()<br />
reslice = sliceLayerLogic.GetReslice()<br />
reslice.SetSlabModeToMax()<br />
reslice.SetSlabNumberOfSlices(600) # use a large number of slices (600) to cover the entire volume<br />
reslice.SetSlabSliceSpacingFraction(0.5) # spacing between slices are 0.5 pixel (supersampling is useful to reduce interpolation artifacts)<br />
sliceNode.Modified()<br />
</pre><br />
<br />
The projected image is available in a ''vtkImageData'' object by calling ''reslice.GetOutput()''.</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/ScriptRepository&diff=46486Documentation/Nightly/ScriptRepository2016-07-09T15:39:23Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
__TOC__<br />
<br />
<br />
=Community-contributed modules=<br />
<br />
Usage: save the .py file to a directory, add the directory to the additional module paths in the Slicer application settings (choose in the menu: Edit / Application settings, click Modules, click >> next to Additional module paths, click Add, and choose the .py file's location).<br />
<br />
==Filters==<br />
* [https://raw.github.com/pieper/VolumeMasker/master/VolumeMasker.py VolumeMasker.py]: Update a target volume with the results of setting all input volume voxels to 0 except for those that correspond to a selected label value in an input label map (Used for example in the volume rendering in [https://www.youtube.com/watch?v=dfu2gugHLHs this video).<br />
<br />
==DICOM==<br />
* [https://gist.github.com/pieper/6186477 dicom header browser] to easily scroll through dicom files using dcmdump.<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/src/BatchProcessing SlicerRT batch processing] to batch convert RT structure sets to labelmap NRRD files.<br />
<br />
==Informatics==<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/sandbox/MarkupsInfoModule/MarkupsInfo.py MarkupsInfo.py]: Compute the total length between all the points of a markup list.<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/sandbox/LineProfile/LineProfile.py LineProfile.py]: Compute intensity profile in a volume along a line.<br />
<br />
=Community-contributed examples=<br />
<br />
Usage: Copy-paste the shown code lines or linked .py file contents into Python console in Slicer.<br />
<br />
==Capture==<br />
* Get a MRML node in the scene based on the node name and call methods of that object. For the MRHead sample data:<br />
vol=slicer.util.getNode('MR*')<br />
vol.GetImageData().GetDimensions()<br />
* Capture the full Slicer screen and save it into a file<br />
img = qt.QPixmap.grabWidget(slicer.util.mainWindow()).toImage()<br />
img.save('c:/tmp/test.png')<br />
* [https://subversion.assembla.com/svn/slicerrt/trunk/SlicerRt/sandbox/CaptureRotationVideo/CaptureRotationVideo.py CaptureRotationVideo.py]: Capture a video of the scene rotating in the 3D view<br />
<br />
==Launching Slicer==<br />
* How to open an .mrb file with Slicer at the command line?<br />
Slicer.exe --python-code "slicer.util.loadScene( 'f:/2013-08-23-Scene.mrb' )"<br />
* How to run a script in the Slicer environment in batch mode (without showing any graphical user interface)?<br />
Slicer.exe --python-code "doSomething; doSomethingElse; etc." --testing --no-splash --no-main-window<br />
<br />
==DICOM==<br />
* How to access tags of DICOM images imported into Slicer? For example, to print the first patient's first study's first series' "0020,0032" field:<br />
db=slicer.dicomDatabase<br />
patientList=db.patients()<br />
studyList=db.studiesForPatient(patientList[0])<br />
seriesList=db.seriesForStudy(studyList[0])<br />
fileList=db.filesForSeries(seriesList[0])<br />
print db.fileValue(fileList[0],'0020,0032')<br />
<br />
* How to access tag of a volume loaded from DICOM? For example, get the patient position stored in a volume:<br />
volumeName='2: ENT IMRT'<br />
n=slicer.util.getNode(volumeName)<br />
instUids=n.GetAttribute('DICOM.instanceUIDs').split()<br />
filename=slicer.dicomDatabase.fileForInstance(instUids[0])<br />
print slicer.dicomDatabase.fileValue(filename,'0018,5100')<br />
<br />
* How to access tag of an item in the Subject Hierachy tree? For example, get the content time tag of a structure set:<br />
rtStructName = '3: RTSTRUCT: PROS'<br />
rtStructNode = slicer.util.getNode(rtStructName)<br />
rtStructSubjectHierarchyNode = slicer.vtkMRMLSubjectHierarchyNode.GetAssociatedSubjectHierarchyNode(rtStructNode)<br />
ctSliceInstanceUids = rtStructSubjectHierarchyNode.GetAttribute('DICOM.ReferencedInstanceUIDs').split()<br />
filename = slicer.dicomDatabase.fileForInstance(ctSliceInstanceUids[0])<br />
print slicer.dicomDatabase.fileValue(filename,'0008,0033')<br />
<br />
==Toolbar functions==<br />
* How to turn on slice intersections in the crosshair menu on the toolbar:<br />
viewNodes = slicer.mrmlScene.GetNodesByClass('vtkMRMLSliceCompositeNode')<br />
viewNodes.UnRegister(slicer.mrmlScene)<br />
viewNodes.InitTraversal()<br />
viewNode = viewNodes.GetNextItemAsObject()<br />
while viewNode:<br />
viewNode.SetSliceIntersectionVisibility(1)<br />
viewNode = viewNodes.GetNextItemAsObject()<br />
<br />
How to find similar functions? For this one I searched for "slice intersections" text in the whole slicer source code, found that the function is implemented in Base\QTGUI\qSlicerViewersToolBar.cxx, then translated the qSlicerViewersToolBarPrivate::setSliceIntersectionVisible(bool visible) method to Python.<br />
<br />
==Manipulating objects in the slice viewer==<br />
* How to define/edit a circular region of interest in a slice viewer?<br />
<br />
Drop two markup points on a slice view and copy-paste the code below into the Python console. After this, as you move the markups you’ll see a circle following the markups.<br />
<br />
# Update the sphere from the fiducial points<br />
def UpdateSphere(param1, param2): <br />
centerPointCoord = [0.0, 0.0, 0.0]<br />
markups.GetNthFiducialPosition(0,centerPointCoord)<br />
circumferencePointCoord = [0.0, 0.0, 0.0]<br />
markups.GetNthFiducialPosition(1,circumferencePointCoord)<br />
sphere.SetCenter(centerPointCoord)<br />
radius=math.sqrt((centerPointCoord[0]-circumferencePointCoord[0])**2+(centerPointCoord[1]-circumferencePointCoord[1])**2+(centerPointCoord[2]-circumferencePointCoord[2])**2)<br />
sphere.SetRadius(radius)<br />
sphere.SetPhiResolution(30)<br />
sphere.SetThetaResolution(30)<br />
sphere.Update()<br />
<br />
# Get a reference to the markup<br />
markups=slicer.util.getNode('F')<br />
# Create the sphere that will intersect the slice viewer<br />
sphere = vtk.vtkSphereSource()<br />
# Initial positioning of the sphere<br />
UpdateSphere(0,0)<br />
# Create model node and add to scene<br />
model = slicer.vtkMRMLModelNode()<br />
model.SetAndObservePolyData(sphere.GetOutput())<br />
modelDisplay = slicer.vtkMRMLModelDisplayNode()<br />
modelDisplay.SetSliceIntersectionVisibility(True) # Show in slice view<br />
modelDisplay.SetVisibility(False) # Hide in 3D view<br />
slicer.mrmlScene.AddNode(modelDisplay)<br />
model.SetAndObserveDisplayNodeID(modelDisplay.GetID())<br />
modelDisplay.SetInputPolyData(model.GetPolyData())<br />
slicer.mrmlScene.AddNode(model) <br />
# Call UpdateSphere whenever the fiducials are changed<br />
markups.AddObserver("ModifiedEvent", UpdateSphere, 2)<br />
<br />
== Add a texture mapped plane to the scene as a model ==<br />
Note that model textures are not exposed in the GUI and are not saved in the scene<br />
<pre><br />
# use dummy image data here<br />
e = vtk.vtkImageEllipsoidSource()<br />
<br />
scene = slicer.mrmlScene<br />
<br />
# Create model node<br />
model = slicer.vtkMRMLModelNode()<br />
model.SetScene(scene)<br />
model.SetName(scene.GenerateUniqueName("2DImageModel"))<br />
<br />
planeSource = vtk.vtkPlaneSource()<br />
model.SetAndObservePolyData(planeSource.GetOutput())<br />
<br />
# Create display node<br />
modelDisplay = slicer.vtkMRMLModelDisplayNode()<br />
modelDisplay.SetColor(1,1,0) # yellow<br />
modelDisplay.SetBackfaceCulling(0)<br />
modelDisplay.SetScene(scene)<br />
scene.AddNode(modelDisplay)<br />
model.SetAndObserveDisplayNodeID(modelDisplay.GetID())<br />
<br />
# Add to scene<br />
modelDisplay.SetAndObserveTextureImageData(e.GetOutput())<br />
scene.AddNode(model) <br />
<br />
<br />
transform = slicer.vtkMRMLLinearTransformNode()<br />
scene.AddNode(transform) <br />
model.SetAndObserveTransformNodeID(transform.GetID())<br />
<br />
vTransform = vtk.vtkTransform()<br />
vTransform.Scale(50,50,50)<br />
vTransform.RotateX(30)<br />
transform.SetAndObserveMatrixTransformToParent(vTransform.GetMatrix())<br />
</pre><br />
<br />
<br />
== Export a model to Blender, including color ==<br />
<br />
<pre><br />
plyFilePath = "/tmp/fibers.ply"<br />
<br />
lineDisplayNode = getNode("*LineDisplay*")<br />
<br />
tuber = vtk.vtkTubeFilter()<br />
tuber.SetInput(lineDisplayNode.GetOutputPolyData())<br />
<br />
tubes = tuber.GetOutput()<br />
tubes.Update()<br />
scalars = tubes.GetPointData().GetArray(0)<br />
scalars.SetName("scalars")<br />
<br />
triangles = vtk.vtkTriangleFilter()<br />
triangles.SetInput(tubes)<br />
<br />
colorNode = lineDisplayNode.GetColorNode()<br />
lookupTable = vtk.vtkLookupTable()<br />
lookupTable.DeepCopy(colorNode.GetLookupTable())<br />
lookupTable.SetTableRange(0,1)<br />
<br />
plyWriter = vtk.vtkPLYWriter()<br />
plyWriter.SetInput(triangles.GetOutput())<br />
plyWriter.SetLookupTable(lookupTable)<br />
plyWriter.SetArrayName("scalars")<br />
<br />
plyWriter.SetFileName(plyFilePath)<br />
plyWriter.Write()<br />
</pre><br />
<br />
== Clone a volume ==<br />
This example shows how to clone the MRHead sample volume, including its pixel data and display settings.<br />
<pre><br />
sourceVolumeNode = slicer.util.getNode('MRHead')<br />
volumesLogic = slicer.modules.volumes.logic()<br />
clonedVolumeNode = volumesLogic.CloneVolume(slicer.mrmlScene, sourceVolumeNode, 'Cloned volume')<br />
</pre><br />
<br />
== Create a new volume ==<br />
This example shows how to create a new empty volume.<br />
<pre><br />
imageSize=[512, 512, 512]<br />
imageSpacing=[1.0, 1.0, 1.0]<br />
voxelType=vtk.VTK_UNSIGNED_CHAR<br />
# Create an empty image volume<br />
imageData=vtk.vtkImageData()<br />
imageData.SetDimensions(imageSize)<br />
imageData.AllocateScalars(voxelType, 1)<br />
thresholder=vtk.vtkImageThreshold()<br />
thresholder.SetInputData(imageData)<br />
thresholder.SetInValue(0)<br />
thresholder.SetOutValue(0)<br />
# Create volume node<br />
volumeNode=slicer.vtkMRMLScalarVolumeNode()<br />
volumeNode.SetSpacing(imageSpacing)<br />
volumeNode.SetImageDataConnection(thresholder.GetOutputPort())<br />
# Add volume to scene<br />
slicer.mrmlScene.AddNode(volumeNode)<br />
displayNode=slicer.vtkMRMLScalarVolumeDisplayNode()<br />
slicer.mrmlScene.AddNode(displayNode)<br />
colorNode = slicer.util.getNode('Grey')<br />
displayNode.SetAndObserveColorNodeID(colorNode.GetID())<br />
volumeNode.SetAndObserveDisplayNodeID(displayNode.GetID())<br />
volumeNode.CreateDefaultStorageNode()<br />
</pre><br />
<br />
== Modify voxels in a volume ==<br />
This example shows how to change voxels values of the MRHead sample volume.<br />
The values will be computed by function f(r,a,s,) = (r-10)*(r-10)+(a+15)*(a+15)+s*s.<br />
<pre><br />
volumeNode=slicer.util.getNode('MRHead')<br />
ijkToRas = vtk.vtkMatrix4x4()<br />
volumeNode.GetIJKToRASMatrix(ijkToRas)<br />
imageData=volumeNode.GetImageData()<br />
extent = imageData.GetExtent()<br />
for k in xrange(extent[4], extent[5]+1):<br />
for j in xrange(extent[2], extent[3]+1):<br />
for i in xrange(extent[0], extent[1]+1):<br />
position_Ijk=[i, j, k, 1]<br />
position_Ras=ijkToRas.MultiplyPoint(position_Ijk)<br />
r=position_Ras[0]<br />
a=position_Ras[1]<br />
s=position_Ras[2] <br />
functionValue=(r-10)*(r-10)+(a+15)*(a+15)+s*s<br />
imageData.SetScalarComponentFromDouble(i,j,k,0,functionValue)<br />
imageData.SetScalarComponentFromFloat(distortionVectorPosition_Ijk[0], distortionVectorPosition_Ijk[1], distortionVectorPosition_Ijk[2], 0, fillValue)<br />
imageData.Modified()<br />
</pre><br />
<br />
== Access values in a DTI tensor volume ==<br />
This example shows how to access individual tensors at the voxel level.<br />
<br />
First load your DWI volume and estimate tensors to produce a DTI volume called ‘Output DTI Volume’<br />
<br />
Then open the python window: View->Python interactor<br />
<br />
Type the following code into the Python window to access all tensor components:<br />
<br />
<pre><br />
volumeNode=slicer.util.getNode('Output DTI Volume')<br />
imageData=volumeNode.GetImageData()<br />
tensors = imageData.GetPointData().GetTensors()<br />
extent = imageData.GetExtent()<br />
idx = 0<br />
for k in xrange(extent[4], extent[5]+1):<br />
for j in xrange(extent[2], extent[3]+1):<br />
for i in xrange(extent[0], extent[1]+1):<br />
tensors.GetTuple9(idx)<br />
idx += 1<br />
</pre><br />
<br />
== Change window/level (brightness/contrast) or colormap of a volume ==<br />
This example shows how to change window/level of the MRHead sample volume.<br />
<pre><br />
volumeNode = getNode('MRHead')<br />
displayNode = volumeNode.GetDisplayNode()<br />
displayNode.AutoWindowLevelOff()<br />
displayNode.SetWindow(50)<br />
displayNode.SetLevel(100)<br />
</pre><br />
<br />
Change color mapping from grayscale to rainbow:<br />
<pre><br />
displayNode.SetAndObserveColorNodeID('vtkMRMLColorTableNodeRainbow')<br />
</pre><br />
<br />
== Manipulate a Slice View ==<br />
<br />
<pre><br />
lm = slicer.app.layoutManager()<br />
red = lm.sliceWidget('Red')<br />
redLogic = red.sliceLogic()<br />
# Print current slice offset position<br />
print redLogic.GetSliceOffset()<br />
# Change slice position<br />
redLogic.SetSliceOffset(20)<br />
</pre><br />
<br />
== Save a series of images from a Slice View ==<br />
<br />
Save the following into a file such as '/tmp/record.py' and then in the slicer python console type "execfile('/tmp/record.py')"<br />
<br />
<pre><br />
layoutName = 'Green'<br />
imagePathPattern = '/tmp/image-%03d.png'<br />
steps = 10<br />
<br />
widget = slicer.app.layoutManager().sliceWidget(layoutName)<br />
view = widget.sliceView()<br />
logic = widget.sliceLogic()<br />
bounds = [0,]*6<br />
logic.GetSliceBounds(bounds)<br />
<br />
for step in range(steps):<br />
offset = bounds[4] + step/(1.*steps) * (bounds[5]-bounds[4])<br />
logic.SetSliceOffset(offset)<br />
view.forceRender()<br />
image = qt.QPixmap.grabWidget(view).toImage()<br />
image.save(imagePathPattern % step)<br />
</pre><br />
<br />
== Show a volume in the Slice Views ==<br />
<br />
<pre><br />
volumeNode = slicer.util.getNode('YourVolumeNode')<br />
applicationLogic = slicer.app.applicationLogic()<br />
selectionNode = applicationLogic.GetSelectionNode()<br />
selectionNode.SetSecondaryVolumeID(volumeNode.GetID())<br />
applicationLogic.PropagateForegroundVolumeSelection(0) <br />
</pre><br />
<br />
or<br />
<br />
<pre><br />
n = slicer.util.getNode('YourVolumeNode')<br />
for color in ['Red', 'Yellow', 'Green']:<br />
slicer.app.layoutManager().sliceWidget(color).sliceLogic().GetSliceCompositeNode().SetForegroundVolumeID(n.GetID())<br />
</pre><br />
<br />
== Center the 3D View on the Scene ==<br />
<pre><br />
layoutManager = slicer.app.layoutManager()<br />
threeDWidget = layoutManager.threeDWidget(0)<br />
threeDView = threeDWidget.threeDView()<br />
threeDView.resetFocalPoint()<br />
</pre><br />
<br />
== Display text in a 3D view or slice view ==<br />
<br />
The easiest way to show information overlaid on a viewer is to use corner annotations.<br />
<br />
<pre><br />
view=slicer.app.layoutManager().threeDWidget(0).threeDView()<br />
# Set text to "Something"<br />
view.cornerAnnotation().SetText(vtk.vtkCornerAnnotation.UpperRight,"Something")<br />
# Set color to red<br />
view.cornerAnnotation().GetTextProperty().SetColor(1,0,0)<br />
# Update the view<br />
view.forceRender()<br />
</pre><br />
<br />
== Turning off interpolation ==<br />
<br />
You can turn off interpolation for newly loaded volumes with this script from Steve Pieper.<br />
<br />
<pre><br />
def NoInterpolate(caller,event):<br />
for node in slicer.util.getNodes('*').values():<br />
if node.IsA('vtkMRMLScalarVolumeDisplayNode'):<br />
node.SetInterpolate(0)<br />
<br />
slicer.mrmlScene.AddObserver(slicer.mrmlScene.NodeAddedEvent, NoInterpolate)<br />
</pre><br />
<br />
The below link explains how to put this in your startup script.<br />
<br />
http://www.na-mic.org/Wiki/index.php/AHM2012-Slicer-Python#Refining_the_code_and_UI_with_slicerrc<br />
<br />
<br />
== Customize viewer layout ==<br />
<br />
Show a custom layout of a 3D view on top of the red slice view:<br />
<br />
<pre><br />
customLayout = ("<layout type=\"vertical\" split=\"true\" >"<br />
" <item>"<br />
" <view class=\"vtkMRMLViewNode\" singletontag=\"1\">"<br />
" <property name=\"viewlabel\" action=\"default\">1</property>"<br />
" </view>"<br />
" </item>"<br />
" <item>"<br />
" <view class=\"vtkMRMLSliceNode\" singletontag=\"Red\">"<br />
" <property name=\"orientation\" action=\"default\">Axial</property>"<br />
" <property name=\"viewlabel\" action=\"default\">R</property>"<br />
" <property name=\"viewcolor\" action=\"default\">#F34A33</property>"<br />
" </view>"<br />
" </item>"<br />
"</layout>")<br />
<br />
customLayoutId=501<br />
<br />
layoutManager = slicer.app.layoutManager()<br />
layoutManager.layoutLogic().GetLayoutNode().AddLayoutDescription(customLayoutId, customLayout) <br />
layoutManager.setLayout(customLayoutId)<br />
</pre><br />
<br />
See description of standard layouts (that can be used as examples) here:<br />
https://github.com/Slicer/Slicer/blob/master/Libs/MRML/Logic/vtkMRMLLayoutLogic.cxx<br />
<br />
== Running an ITK filter in Python using SimpleITK ==<br />
Open the "Sample Data" module and download "MR Head", then paste the following snippet in Python interactor:<br />
<pre><br />
inputImage = sitkUtils.PullFromSlicer('MRHead')<br />
filter = sitk.SignedMaurerDistanceMapImageFilter()<br />
outputImage = filter.Execute(inputImage)<br />
sitkUtils.PushToSlicer(outputImage,'outputImage')<br />
</pre><br />
<br />
More information:<br />
* See the SimpleITK documentation for SimpleITK examples: http://www.itk.org/SimpleITKDoxygen/html/examples.html<br />
* sitkUtils in Slicer is used for pushing and pulling images from Slicer to SimpleITK: https://github.com/Slicer/Slicer/blob/master/Base/Python/sitkUtils.py<br />
<br />
== Get current mouse coordinates in a slice view ==<br />
<br />
You can get 3D (RAS) coordinates of the current mouse cursor from the crosshair singleton node as shown in the example below:<br />
<br />
<pre><br />
def onMouseMoved(observer,eventid): <br />
ras=[0,0,0]<br />
crosshairNode.GetCursorPositionRAS(ras)<br />
print(ras)<br />
<br />
crosshairNode=slicer.util.getNode('Crosshair') <br />
crosshairNode.AddObserver(slicer.vtkMRMLCrosshairNode.CursorPositionModifiedEvent, onMouseMoved)<br />
</pre><br />
<br />
== Thick slab reconstruction and maximum/minimum intensity volume projections ==<br />
<br />
Set up 'red' slice viewer to show thick slab reconstructed from 3 slices:<br />
<pre><br />
sliceNode = slicer.mrmlScene.GetNodeByID('vtkMRMLSliceNodeRed')<br />
appLogic = slicer.app.applicationLogic()<br />
sliceLogic = appLogic.GetSliceLogic(sliceNode)<br />
sliceLayerLogic = sliceLogic.GetBackgroundLayer()<br />
reslice = sliceLayerLogic.GetReslice()<br />
reslice.SetSlabModeToMean()<br />
reslice.SetSlabNumberOfSlices(10) # mean of 10 slices will computed<br />
reslice.SetSlabSliceSpacingFraction(0.3) # spacing between each slice is 0.3 pixel (total 10 * 0.3 = 3 pixel neighborhood)<br />
sliceNode.Modified()<br />
</pre><br />
<br />
Set up 'red' slice viewer to show maximum intensity projection (MIP):<br />
<pre><br />
sliceNode = slicer.mrmlScene.GetNodeByID('vtkMRMLSliceNodeRed')<br />
appLogic = slicer.app.applicationLogic()<br />
sliceLogic = appLogic.GetSliceLogic(sliceNode)<br />
sliceLayerLogic = sliceLogic.GetBackgroundLayer()<br />
reslice = sliceLayerLogic.GetReslice()<br />
reslice.SetSlabModeToMax()<br />
reslice.SetSlabNumberOfSlices(600) # use a large number of slices (600) to cover the entire volume<br />
reslice.SetSlabSliceSpacingFraction(0.5) # spacing between slices are 0.5 pixel (supersampling is useful to reduce interpolation artifacts)<br />
sliceNode.Modified()<br />
</pre><br />
<br />
The projected image is available in a ''vtkImageData'' object by calling ''reslice.GetOutput()''.</div>Laurenhttps://www.slicer.org/w/index.php?title=Documentation/Nightly/Modules/FiberBundleToLabelMap&diff=46469Documentation/Nightly/Modules/FiberBundleToLabelMap2016-07-06T20:52:16Z<p>Lauren: </p>
<hr />
<div><noinclude>{{documentation/versioncheck}}</noinclude><br />
<!-- ---------------------------- --><br />
{{documentation/{{documentation/version}}/module-header}}<br />
<!-- ---------------------------- --><br />
<br />
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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}<br />
{{documentation/{{documentation/version}}/module-introduction-start|{{documentation/modulename}}}}<br />
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<br />
<br />
Title: Fiber Bundle to Label Map<br />
<br />
Author(s)/Contributor(s): Steve Pieper (SPL, Isomics, Inc.), Isaiah Norton (SPL, LMI, BWH, SlicerDMRI)<br />
<br />
License: 3D Slicer Contribution and Software License Agreement<br />
<br />
Acknowledgements: The SlicerDMRI developers gratefully acknowledge funding for this project provided by NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research), NIH P41EB015898 (National Center for Image-Guided Therapy) and NIH P41EB015902 (Neuroimaging Analysis Center), as well as the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149.<br />
<br />
Contact: slicer-users@bwh.harvard.edu<br />
<br />
Website: http://slicerdmri.github.io/<br />
<br />
{{documentation/{{documentation/version}}/module-introduction-row}}<br />
{{documentation/{{documentation/version}}/module-introduction-logo-gallery<br />
|Image:SlicerDMRIScreenshot.jpg|SlicerDMRI<br />
|Image:Logo-splnew.jpg|Surgical Planning Laboratory<br />
|Image:NAC-logo.png|NAC<br />
|Image:CC label.png| Corpus callosum (CC) tracts<br />
|Image:CC tract label.png| Label map from the corpus callosum (CC) tracts.<br />
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{{documentation/{{documentation/version}}/module-section|Module Description}}<br />
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<br />
[[File:FiberBundleToLabelMap-Screen Shot 2013-07-08 at 11.27.12 AM.png|thumb|300px|Example of the label map generated from a set of fibers]]<br />
<br />
This module sets the specified label value in the label map at every vertex in each of the fibers in a bundle. <br />
<br />
This module first upsamples points along the fiber bundle in order to get better voxel coverage. <br />
<br />
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}<br />
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<br />
* Fiber Bundle<br />
** Pick a fiber bundle to rasterize<br />
* Target Label Map<br />
** The fibers will be painted into this label map. Note that this will add to and overwrite existing data, but does not clear the label map to zero first.<br />
* Label Value<br />
** Numerical value to be written into the label map.<br />
<br />
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}<br />
[[Documentation/{{documentation/version}}/Modules/ModelToLabelMap|Model To Label Map]]<br />
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* http://slicerdmri.github.io/<br />
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{{documentation/{{documentation/version}}/module-section|Information for Developers}}<br />
This is a python scripted module.<br />
<br />
* https://github.com/SlicerDMRI<br />
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<!-- ---------------------------- --></div>Lauren