# 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, demian@bwh.harvard.edu Contributor1: Raúl San José-Estepar Contributor2: Lauren O'Donnel

# 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