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Home < Documentation < Nightly < Modules < DWModeling

For the stable Slicer documentation, visit the 4.10 page.

Introduction and Acknowledgements

SlicerProstate Logo 1.0 128x128.png

Extension: SlicerProstate
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)
  • Quantitative Image Informatics for Cancer Research (QIICR) (U24 CA180918, PIs Kikinis and Fedorov).

Authors: Andrey Fedorov (SPL), Alireza Mehrtash (SPL)
Contact: Andrey Fedorov, <email></email>

License: Slicer License

National Center for Image Guided Therapy (NCIGT)  
Quantitative Image Informatics for Cancer Research  
Surgical Planning Laboratory (SPL)  

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 DICOMBrowser. The module currently supports mono-exponential, bi-exponential and kurtosis DW models, see [1] for further details on the models.

Modeling results can be explored (interactive curve plotting of the input and fitted data) using 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].

Use Cases

  • tissue characterization from DWI MRI
  • quantitative image analysis
  • treatment response assessment


None at this time ... stay tuned!

Panels and their use


  • Input image: multivolume node containing multi-b-value trace image loaded using DICOM module
  • Model: mono-, bi-exponential or kurtosis model should be selected
  • Input mask: segmentation of the region of interest (optional); if specified, model fitting will be performed only within the specified mask
  • B-values to include: list of b-values that should be used in the fitting process (optional); this parameter is used only if not empty
  • B-values to exclude: list of b-values that should NOT be used in the fitting process (optional); this parameter is used only if not empty
  • Fitted volume: (output) multi-volume containing the fitted model sampled at the b-values of the input dataset
  • Quality of fit volume: (optionla) R-squared

Related Modules


[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;

[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.

Information for Developers