Difference between revisions of "Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements"

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*MidEigenvalue: the second smallest eigenvalue of the diffusion tensor (also called lambda 2).
 
*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).
 
*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.  
+
*ParallelDiffusivity: this is equal to the first (maximum) eigenvalue, and also known as lambda 1 and axial diffusivity (AD).  So this is equal to MaxEigenvalue, above.
*PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3.  
+
*PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3. This is also known as radial diffusivity (RD).
  
 
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Revision as of 21:12, 14 September 2016

Home < Documentation < Nightly < Modules < DiffusionTensorScalarMeasurements

For the stable Slicer documentation, visit the 4.10 page.

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

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 (maximum) eigenvalue, and also known as lambda 1 and axial diffusivity (AD). So this is equal to MaxEigenvalue, above.
  • PerpendicularDiffusivity: this is the average of the smaller two eigenvalues, lambda 2 and lambda 3. This is also known as radial diffusivity (RD).

Similar Modules

  • DWIToDTIEstimation
  • FiberTractMeasurements

References

Information for Developers