For the stable Slicer documentation, visit the 4.10 page.
Introduction and Acknowledgements
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  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 .
- 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
 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
 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