Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements

For the latest Slicer documentation, visit the read-the-docs.

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> Contributor1: Raúl San José-Estepar Contributor2: Lauren O'Donnel

Surgical Planning Laboratory   NAC

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:
• Use Case 2:

Tutorials

Links to tutorials that use this module

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).
• MaxEigenvalueProjectionX: this is the max eigenvalue (lambda 1) times the x component of the major eigenvector.
• MaxEigenvalueProjectionY: this is the max eigenvalue (lambda 1) times the y component of the major eigenvector.
• MaxEigenvalueProjectionZ: this is the max eigenvalue (lambda 1) times the z component of the major eigenvector.
• RAIMaxEigenvecX: relative anisotropy times the x component of the major eigenvector.
• RAIMaxEigenvecY: relative anisotropy times the y component of the major eigenvector.
• RAIMaxEigenvecZ: relative anisotropy times the z component of the major eigenvector.
• D11: one of the values on the diagonal of the tensor: D11 is the upper left corner.
• D22: one of the values on the diagonal of the tensor: D22 is the middle.
• D33: one of the values on the diagonal of the tensor: D33 is the lower right corner.
• 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

• 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