Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements

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Introduction and Acknowledgements

Title: Diffusion Tensor Scalar Maps Author(s)/Contributor(s): Raul San Jose, Lauren O'Donnell, Demian Wassermann, Isaiah Norton, Alex Yarmarkovich (SPL, LMI, BWH, SlicerDMRI) License: 3D Slicer Contribution and Software License Agreement Acknowledgements: The SlicerDMRI developers gratefully acknowledge funding for this project provided by NIH NCI ITCR U01CA199459 (Open Source Diffusion MRI Technology For Brain Cancer Research), NIH P41EB015898 (National Center for Image-Guided Therapy) and NIH P41EB015902 (Neuroimaging Analysis Center), as well as 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. Contact: <email>slicer-users@bwh.harvard.edu</email> Website: http://slicerdmri.github.io/

SlicerDMRI   Surgical Planning Laboratory   NAC   Diffusion Tensor Trace   Diffusion Tensor FA

Module Description

Compute scalar measures from a diffusion tensor dataset. Available measurements include fractional anisotropy, trace, and more.

Use Cases

Most frequently used for these scenarios:

• Use Case 1: Create FA (fractional anisotropy image)
• Use Case 2: Quantify FA or another measure in a region of interest (using also the Editor and Quantification->Label Statistics)

Tutorials

Links to tutorials that use this module

• Slicer4 Diffusion Tensor Imaging Tutorial: https://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Training#Slicer4_Diffusion_Tensor_Imaging_Tutorial

Panels and their use

Parameters:

• Settings: Input/output parameters
  • Input DTI Volume (inputVolume): Input DTI volume
  • Output Volume (outputScalar): Scalar volume derived from tensor
  • Scalar Measurement (operation): Type of scalar measurement to perform

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

• Trace: trace of the diffusion tensor (equal to the sum of its eigenvalues). Trace = 3 * MD, where MD is mean diffusivity.
• Determinant: determinant of diffusion tensor.
• RelativeAnisotropy: ratio of the anisotropic part of the diffusion tensor to the isotropic part (Basser, 1994)
• FractionalAnisotropy: the degree of anisotropy of diffusion (Between 0 and 1 where 0 = completely isotropic, 1 = completely anisotropic). This measure can be thought of as quantifying how far the shape of diffusion is from a sphere.
• Mode: Quantifies how linear or planar the diffusion is (cigar vs. pancake shape). FA and mode are orthogonal measures. (Ennis & Kindlmann, 2006).
• LinearMeasure: is high in "cigar-shape" tensors, where the first eigenvalue is much larger than the others (Westin, 2002).
• PlanarMeasure: is high in "pancake-shape" planar tensors, where the smallest eigenvalue is much less than the others (Westin, 2002).
• SphericalMeasure: is high where all three eigenvalues are equal, giving a "sphere-shape" of the tensor (Westin, 2002).
• MinEigenvalue: the smallest of the three eigenvalues of the diffusion tensor (also called lambda 3).
• MidEigenvalue: the second smallest eigenvalue of the diffusion tensor (also called lambda 2).
• MaxEigenvalue: the largest of the three eigenvalues of the diffusion tensor (also called lambda 1).
• ParallelDiffusivity: this is equal to the first eigenvalue, lambda 1.
• PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3.

Similar Modules

• DWIToDTIEstimation
• FiberTractMeasurements

References

• Basser, 1994
• Westin, 2002
• Ennis & Kindlmann, 2006
• http://slicerdmri.github.io/

Information for Developers

• https://github.com/SlicerDMRI