Documentation/Nightly/Modules/DiffusionWeightedVolumeMasking

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Introduction and Acknowledgements

This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website. Author: Demian Wassermann, SPL, LMI, PNL, Brigham and Women's Hospital, Harvard Medical School Contact: Demian Wassermann, <email>demian@bwh.harvard.edu</email>

SlicerDMRI   Surgical Planning Laboratory   NAC   Diffusion Tensor Trace   Diffusion Tensor FA

Module Description

Creates a brain mask from a diffusion weighted image volume. The mask can be used during diffusion tensor estimation or tractography seeding.

The brain mask is computed by averaging all baseline (non-diffusion-weighted) images, applying the Otsu thresholding algorithm to segment tissue voxels, and then removing small unconnected regions.

Use Cases

Most frequently used for these scenarios:

• Use Case 1:
• Use Case 2:

Tutorials

Links to tutorials that use this module

Panels and their use

Parameters:

• IO: Input/output parameters
  • Input DWI Volume (inputVolume): Input DWI volume
  • Output Baseline Volume (outputBaseline): Extracted baseline volume
  • Output Diffusion Brain Mask (thresholdMask): Output Diffusion Brain Mask
• Mask Settings:
  • Baseline B-Value Threshold Parameter (baselineBValueThreshold): Volumes with B-value below this threshold will be considered baseline images and included in mask calculation.
  • Remove Islands in Brain Mask (removeIslands): Removes disconnected regions from brain mask.

List of parameters generated transforming this XML file using this XSL file. To update the URL of the XML file, edit this page.

Similar Modules

• Point to other modules that have similar functionality

References

Publications related to this module go here. Links to pdfs would be useful. For extensions: link to the source code repository and additional documentation