Documentation/Nightly/Modules/DiffusionTensorScalarMeasurements

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Introduction and 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> Contributors: Raúl San José-Estepar, Lauren O'Donnell, Demian Wassermann, Isaiah Norton, Alex Yarmarkovich

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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