Documentation/4.4/Modules/DiffusionTensorScalarMeasurements

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

Surgical Planning Laboratory  
NAC  

Module Description

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:





  ()
 
 
   
     * ': 
     
       ** ': 
       
        *** ': 
       
     
   
 


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

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