UpdateBot: Nightly -> 4.8

2017-10-18T07:04:40Z

Nightly -> 4.8

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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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[[Image:SlicerProstate_Logo_1.0_128x128.png]]
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Extension: [[Documentation/{{documentation/version}}/Extensions/SlicerProstate|SlicerProstate]]<br>
Acknowledgments:
This work supported in part the National Institutes of Health, National Cancer Institute through the following grants:
* Quantitative MRI of prostate cancer as a biomarker and guide for treatment, Quantitative Imaging Network (U01 CA151261, PI Fennessy)
* Enabling technologies for MRI-guided prostate interventions (R01 CA111288, PI Tempany)
* The National Center for Image-Guided Therapy (P41 EB015898, PI Tempany)
* [http://qiicr.org Quantitative Image Informatics for Cancer Research (QIICR)] (U24 CA180918, PIs Kikinis and Fedorov).<br>
Authors: Andrey Fedorov ({{collaborator|name|spl}}), Alireza Mehrtash ({{collaborator|name|spl}})<br>
Contact: Andrey Fedorov, <email>fedorov@bwh.harvard.edu</email><br>

License: [http://www.slicer.org/pages/LicenseText Slicer License]
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{{documentation/{{documentation/version}}/module-section|Module Description}}

The purpose of this module is to provide estimation of DWI quantitative parameters using commonly diffusion models. Input data should be a trace DWI image correctly identified as a multivolume by b-value by the Slicer [[Documentation/Nightly/Modules/DICOM|DICOMBrowser]]. The module currently supports mono-exponential, bi-exponential, kurtosis, stretched exponential and gamma distribution DW models, see references for further details on the models.

Modeling results can be explored (interactive curve plotting of the input and fitted data) using [[Documentation/Nightly/Modules/MultiVolumeExplorer|MultiVolumeExplorer]] module.

Development of this module was motivated by the application in DWI of prostate cancer. Testing was done on the DWI MRI data collected on a 3T GE Discovery 750w platform (trace images from DWI obtained with 3 orthogonal diffusion directions) at Brigham and Women's hospital. The module was used to obtain results presented in [2].


{{documentation/{{documentation/version}}/module-section|Use Cases}}
* tissue characterization from DWI MRI
* quantitative image analysis
* treatment response assessment

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|[[Image:Fitting_example_plot.png|thumb|500px|Example of data fitting using bi-exponential model, as shown in MultiVolumeExplorer module]]
|[[Image:Fitting_example_plot_log.png|thumb|500px|Example of data fitting using bi-exponential model, as shown in MultiVolumeExplorer module, log scale]]
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{{documentation/{{documentation/version}}/module-section|Tutorials}}
None at this time ... stay tuned!


{{documentation/{{documentation/version}}/module-section|Panels and their use}}

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{{documentation/{{documentation/version}}/module-section|Related Modules}}
* [[Documentation/Nightly/Modules/MultiVolumeExplorer|MultiVolumeExplorer]]
* [[Documentation/Nightly/Modules/PkModeling|PkModeling]]
* [[Documentation/Nightly/Modules/LabelStatistics|LabelStatistics]]


{{documentation/{{documentation/version}}/module-section|References}}
[1] Toivonen J, Merisaari H, Pesola M, Taimen P, Boström PJ, Pahikkala T, et al. Mathematical models for diffusion-weighted imaging of prostate cancer using b values up to 2000 s/mm2: Correlation with Gleason score and repeatability of region of interest analysis. Magn Reson Med. 2014; http://onlinelibrary.wiley.com/doi/10.1002/mrm.25482/abstract

[2] Kobus T., Fedorov A., Tempany C.M., Mulkern R.V., Dunne R., Maier S.E. Bi-exponential Diffusion Analysis in Normal Prostate and Prostate Cancer: Transition Zone and Peripheral Zone Considerations. Proc. of ISMRM 2015. http://www.spl.harvard.edu/abstracts/item/view/168

[3] Oshio K, Shinmoto H, Mulkern RV. Interpretation of diffusion MR imaging data using a gamma distribution model. Magn Reson Med Sci. 2014;13: 191–195. doi:10.2463/mrms.2014-0016

[4] Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, Hyde JS. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med. 2003;50: 727–734. doi:10.1002/mrm.10581

[5] The Minitab Blog: Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit? http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
* Source code: https://github.com/SlicerProstate/SlicerProstate/tree/master/DWModeling


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Documentation/4.8/Modules/DWModeling - Revision history

UpdateBot: Nightly -> 4.8