Difference between revisions of "Modules:ROISeeding-Documentation-3.4"

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[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
 
[[Documentation-3.4|Return to Slicer 3.4 Documentation]]
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[[Announcements:Slicer3.4#Highlights|Gallery of New Features]]
 
__NOTOC__
 
__NOTOC__
 
===Module Name===
 
===Module Name===
MyModule
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Labelmap Seeding
  
 
{|
 
{|
|[[Image:screenshotBlank.png|thumb|280px|Caption 1]]
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|[[Image:roi_tract.jpg|thumb|280px|Caption 1]]
 
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* We want to study the white matter integrate across a population for a given tract. ROISeeding can be used to carry this study.  
 
* We want to study the white matter integrate across a population for a given tract. ROISeeding can be used to carry this study.  
**First, a region of interest in the tract that is under study has to be defined for each subject, for example by manually delineating the ROI using the Editor module. The Fractional Anisotropy (FA) Volume can be used as reference volume to trace the ROI. The FA volume can be computed using [http://www.slicer.org/slicerWiki/index.php/Modules:DiffusionTensorScalarMeasurements-Documentation-3.4 Diffusion Tensor Scalar Measurements].  
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**First, a region of interest in the tract that is under study has to be defined for each subject, for example by manually delineating the ROI using the Editor module. The Fractional Anisotropy (FA) Volume can be used as reference volume to trace the ROI. The FA volume can be computed using [[Modules:DiffusionTensorScalarMeasurements-Documentation-3.4|Diffusion Tensor Scalar Measurements]].  
**Second, if the DTI volume is not direcly available, the DWI has to be loaded and the tensor has to be created using the [http://www.slicer.org/slicerWiki/index.php/Modules:DiffusionTensorEstimation-Documentation-3.4 | Diffusion Tensor Estimation].  
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**Second, if the DTI volume is not direcly available, the DWI has to be loaded and the tensor has to be created using the [[Modules:DiffusionTensorEstimation-Documentation-3.4|| Diffusion Tensor Estimation]].  
 
**Third, set up the ROI Seeding using the DTI volume and the ROI labelmap as main volume inputs.  
 
**Third, set up the ROI Seeding using the DTI volume and the ROI labelmap as main volume inputs.  
 
**Fourth, choose the tractography parameters. The stopping criteria are the more important parameters. Depending on the setting different results can be obtained, for example, fiber can be longer or shorter.
 
**Fourth, choose the tractography parameters. The stopping criteria are the more important parameters. Depending on the setting different results can be obtained, for example, fiber can be longer or shorter.
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=== Caveats and other limitations ===
 
=== Caveats and other limitations ===
ROISeeding module does not work when the labelmap dimensions do not match the DTI volume dimensions. The work-around is to resample labelmap using [http://www.slicer.org/slicerWiki/index.php/Modules:ResampleVolume2-Documentation-3.4 | Filtering -> Resample Volume 2].
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ROISeeding module does not work when the labelmap dimensions do not match the DTI volume dimensions. The work-around is to resample labelmap using [[Modules:ResampleVolume2-Documentation-3.4|| Filtering -> Resample Volume 2]].
  
 
== Development ==
 
== Development ==
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===Source code & documentation===
 
===Source code & documentation===
  
Customize following [http://www.na-mic.org/ViewVC/index.cgi/ links] for your module.
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Information about the DTMRI infrastructure in Slicer 3 and related classess can be found [[Slicer3:DTMRI|here]]
 
 
[http://www.na-mic.org/Slicer/Documentation/Slicer3/html/ Links] to documentation generated by doxygen.
 
  
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[http://www.na-mic.org/Slicer/Documentation/Slicer3-doc/html/ Links] to documentation generated by doxygen.
  
 
== More Information ==  
 
== More Information ==  

Latest revision as of 02:26, 27 November 2019

Home < Modules:ROISeeding-Documentation-3.4

Return to Slicer 3.4 Documentation

Gallery of New Features

Module Name

Labelmap Seeding

Caption 1

General Information

Module Type & Category

Type: CLI

Category: Base or (Filtering, Registration, etc.)

Authors, Collaborators & Contact

Module Description

ROI Seeding is a tractography implementation that allows a user to seed tracts from a region of interest (ROI). The ROI is defined as a labelmap and has to be provided by the user. One approach to generate the ROI is by means of the Editor module.

Usage

Examples, Use Cases & Tutorials

  • We want to study the white matter integrate across a population for a given tract. ROISeeding can be used to carry this study.
    • First, a region of interest in the tract that is under study has to be defined for each subject, for example by manually delineating the ROI using the Editor module. The Fractional Anisotropy (FA) Volume can be used as reference volume to trace the ROI. The FA volume can be computed using Diffusion Tensor Scalar Measurements.
    • Second, if the DTI volume is not direcly available, the DWI has to be loaded and the tensor has to be created using the | Diffusion Tensor Estimation.
    • Third, set up the ROI Seeding using the DTI volume and the ROI labelmap as main volume inputs.
    • Fourth, choose the tractography parameters. The stopping criteria are the more important parameters. Depending on the setting different results can be obtained, for example, fiber can be longer or shorter.

Quick Tour of Features and Use

The module is very straighforward to use:

  • Input DTI volume:set the DTI volume that is going to be used for tractography
  • Parameters panel:
  • Output panel:
  • Viewing panel:

Caveats and other limitations

ROISeeding module does not work when the labelmap dimensions do not match the DTI volume dimensions. The work-around is to resample labelmap using | Filtering -> Resample Volume 2.

Development

Dependencies

None

Known bugs

Follow this link to the Slicer3 bug tracker.


Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Information about the DTMRI infrastructure in Slicer 3 and related classess can be found here

Links to documentation generated by doxygen.

More Information

Acknowledgment

Include funding and other support here.

References

Publications related to this module go here. Links to pdfs would be useful.