Difference between revisions of "Documentation/Nightly/Modules/DWModeling"
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−  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 bvalue by the Slicer [[Documentation/Nightly/Modules/DICOMDICOMBrowser]]. The module currently supports monoexponential, biexponential and  +  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 bvalue by the Slicer [[Documentation/Nightly/Modules/DICOMDICOMBrowser]]. The module currently supports monoexponential, biexponential, 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/MultiVolumeExplorerMultiVolumeExplorer]] module.  Modeling results can be explored (interactive curve plotting of the input and fitted data) using [[Documentation/Nightly/Modules/MultiVolumeExplorerMultiVolumeExplorer]] module.  
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+  [[Image:Fitting_example_plot.pngthumb500pxExample of data fitting using biexponential model, as shown in MultiVolumeExplorer module]]  
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[2] Kobus T., Fedorov A., Tempany C.M., Mulkern R.V., Dunne R., Maier S.E. Biexponential 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  [2] Kobus T., Fedorov A., Tempany C.M., Mulkern R.V., Dunne R., Maier S.E. Biexponential 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.20140016  
+  
+  [4] Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, Hyde JS. Characterization of continuously distributed cortical water diffusion rates with a stretchedexponential model. Magn Reson Med. 2003;50: 727–734. doi:10.1002/mrm.10581  
+  
+  [5] The Minitab Blog: Regression Analysis: How Do I Interpret Rsquared and Assess the GoodnessofFit? http://blog.minitab.com/blog/adventuresinstatistics/regressionanalysishowdoiinterpretrsquaredandassessthegoodnessoffit  
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Latest revision as of 12:30, 21 March 2016
Home < Documentation < Nightly < Modules < DWModeling
For the stable Slicer documentation, visit the 4.10 page. 
Introduction and Acknowledgements

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 bvalue by the Slicer DICOMBrowser. The module currently supports monoexponential, biexponential, 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 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
Tutorials
None at this time ... stay tuned!
Panels and their use
Parameters:
 Input: Input parameters
 Input image (imageName): Input DWI MRI trace multivolume image
 Model (modelName): Select the mathematical model used to fit the data
 Input mask (maskName): Input mask. Optional; if not specified, fitting will be done at all voxels.
 Bvalues to include (bValuesToInclude): List of integers corresponding to the bvalues that should be included in fitting data. Note that only one of the two lists (inlusion or exclusion) should be populated, or both can be empty, in which case all bvalues will be used. Optional; if not defined, all bvalues will be used.
 Bvalues to exclude (bValuesToExclude): List of integers corresponding to the bvalues that should be excluded from fitting data. Note that only one of the two lists (inlusion or exclusion) should be populated, or both can be empty, in which case all bvalues will be used. Optional; if not defined, all bvalues will be used.
 Output common to all models:
 Fitted volume (fittedVolumeFileName): Output volume containing the values of the fitted function
 R^2 quality of fit volume (rsqrVolumeFileName): Output volume containing the R^2 measure of the quality of fit. This measure is calculated only for the bvalues used in the fitting process.
 SSD for the fitted bvalues (ssdFittedVolumeFileName): Volume with the pixelwise sum of squared differences (SSD) map that takes into consideration only those bvalues that were used in the fitting process
 SSD for all bvalues (ssdVolumeFileName): Volume with the pixelwise sum of squared differences (SSD) map that takes into consideration all bvalues. Note that this map will be identical to the previous one if the fitting procedure utilized all bvalues
 Chi squared for the fitted bvalues (csFittedVolumeFileName): Volume with the pixelwise chi squared map that takes into consideration only those bvalues that were used in the fitting process
 Chi squared for all bvalues (csVolumeFileName): Volume with the pixelwise pixelwise chi squared map that takes into consideration all bvalues. Note that this map will be identical to the previous one if the fitting procedure utilized all bvalues
 MonoExponential model output:
 MonoExp diffusion map (adcMapFileName): Diffusion coefficient map of the monoexponential model
 BiExponential model outputs:
 Slow diffusion map (slowDiffMapFileName): Slow diffusion coefficient map of the biexponential model
 Fast diffusion map (fastDiffMapFileName): Fast diffusion coefficient map of the biexponential model
 Fast diffusion fraction map (fastDiffFractionMapFileName): Fast diffusion fraction map of the biexponential model
 Kurtosis model outputs:
 Kurtosis diffusion map (kurtosisDiffMapFileName): Diffusion coefficient map of the kurtosis model
 Kurtosis map (kurtosisMapFileName): Kurtosos map
 Gamma distribution model outputs: See Oshio et al. 2014
 Theta parameter map (thetaMapFileName): Theta parameter of the gamma distribution model
 k map (kMapFileName): k map of the gamma distribution model
 Mode map (modeMapFileName): Mode map of the gamma distribution model ( (k1)*theta )
 Stretched exponential model outputs: See Bennett et al. 2003
 DDC map (DDCMapFileName): Distributed Diffusion Coefficient map
 Stretching parameter map (alphaMapFileName): Stretching parameter (alpha) map
 Initialization: Modelspecific initial parameters
 Monoexponential model (monoExpInitParameters): List of initial model parameters in the following format (all numbers are floating point):initialScale,initialADC
 Biexponential model (biExpInitParameters): List of initial model parameters in the following format (all numbers are floating point):initialScale,initialFastDiffusionFraction,initialSlowDiffusionCoefficient,initialFastDiffusionCoefficient
 Kurtosis model (kurtosisInitParameters): List of initial model parameters in the following format (all numbers are floating point):initialScale,initialKurtosis,initialKurtosisDiffusion
 Stretched exponential model parameters (stretchedExpInitParameters): List of initial model parameters in the following format (all numbers are floating point):initialScale,initialDDC,initialAlpha
 Gamma distribution model (gammaInitParameters): List of initial model parameters in the following format (all numbers are floating point):initialScale,initialK,initialTheta
List of parameters generated transforming this XML file using this XSL file. To update the URL of the XML file, edit this page.
Related Modules
References
[1] Toivonen J, Merisaari H, Pesola M, Taimen P, Boström PJ, Pahikkala T, et al. Mathematical models for diffusionweighted 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. Biexponential 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.20140016
[4] Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, Hyde JS. Characterization of continuously distributed cortical water diffusion rates with a stretchedexponential model. Magn Reson Med. 2003;50: 727–734. doi:10.1002/mrm.10581
[5] The Minitab Blog: Regression Analysis: How Do I Interpret Rsquared and Assess the GoodnessofFit? http://blog.minitab.com/blog/adventuresinstatistics/regressionanalysishowdoiinterpretrsquaredandassessthegoodnessoffit