fsl.data.dtifit

This module provides the DTIFitTensor class, which encapsulates the diffusion tensor data generated by the FSL dtifit tool.

The following utility functions are also defined:

getDTIFitDataPrefix	Returns the prefix (a.k,a, base name) used for the dtifit file names in the given directory, or None if the dtifit files could not be identified.
isDTIFitPath	Returns True if the given directory path looks like it contains dtifit data, False otherwise.
looksLikeTensorImage	Returns True if the given Image looks like it could contain tensor matrix data, False otherwise.
decomposeTensorMatrix	Decomposes the given numpy array into six separate arrays, containing the eigenvectors and eigenvalues of the tensor matrix decompositions.

fsl.data.dtifit.getDTIFitDataPrefix(path)

Returns the prefix (a.k,a, base name) used for the dtifit file names in the given directory, or None if the dtifit files could not be identified.

fsl.data.dtifit.isDTIFitPath(path)

Returns True if the given directory path looks like it contains dtifit data, False otherwise.

fsl.data.dtifit.looksLikeTensorImage(image)

Returns True if the given Image looks like it could contain tensor matrix data, False otherwise.

fsl.data.dtifit.decomposeTensorMatrix(data)

Decomposes the given numpy array into six separate arrays, containing the eigenvectors and eigenvalues of the tensor matrix decompositions.

Parameters:image – A 4D numpy array with 6 volumes, which contains the unique elements of diffusion tensor matrices at every voxel. Returns:A tuple containing the principal eigenvectors and eigenvalues of the tensor matrix.

class fsl.data.dtifit.DTIFitTensor(path)

Bases: fsl.data.image.Nifti

The DTIFitTensor class is able to load and encapsulate the diffusion tensor data generated by the FSL dtifit tool. The DtiFitTensor class supports tensor model data generated by dtifit, where the eigenvectors and eigenvalues of the tensor matrices have been saved as six separate NIFTI images.

V1()

V2()

V3()

L1()

L2()

L3()