fsl.data.dtifit

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

There are also conversion tools between the diffusion tensors defined in 3 formats:

• (…, 3, 3) array with the full diffusion tensor

• (…, 6) array with the unique components (Dxx, Dxy, Dxz, Dyy, Dyz, Dzz)

• Tuple with the eigenvectors and eigenvalues (V1, V2, V3, L1, L2, L3)

Finally 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.eigendecompositionToTensor(V1, V2, V3, L1, L2, L3)

Converts the eigenvalues/eigenvectors into a 3x3 diffusion tensor

Parameters:

• V1 – (…, 3) shaped array with the first eigenvector

• V2 – (…, 3) shaped array with the second eigenvector

• V3 – (…, 3) shaped array with the third eigenvector

• L1 – (…, ) shaped array with the first eigenvalue

• L2 – (…, ) shaped array with the second eigenvalue

• L3 – (…, ) shaped array with the third eigenvalue

Returns:

(…, 3, 3) array with the diffusion tensor

fsl.data.dtifit.tensorToEigendecomposition(matrices)

Decomposes the 3x3 diffusion tensor into eigenvalues and eigenvectors

Parameters:

matrices – (…, 3, 3) array-like with diffusion tensor

Returns:

Tuple containing the eigenvectors and eigenvalues (V1, V2, V3, L1, L2, L3)

fsl.data.dtifit.tensorToComponents(matrices)

Extracts the 6 unique components from a 3x3 diffusion tensor

Parameters:

matrices – (…, 3, 3) array-like with diffusion tensors

Returns:

(…, 6) array with the unique components sorted like Dxx, Dxy, Dxz, Dyy, Dyz, Dzz

fsl.data.dtifit.componentsToTensor(components)

Creates 3x3 diffusion tensors from the 6 unique components

Parameters:

components – (…, 6) array-like with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz

Returns:

(…, 3, 3) array with the diffusion tensors

fsl.data.dtifit.eigendecompositionToComponents(V1, V2, V3, L1, L2, L3)

Converts the eigenvalues/eigenvectors into the 6 unique components of the diffusion tensor

Parameters:

• V1 – (…, 3) shaped array with the first eigenvector

• V2 – (…, 3) shaped array with the second eigenvector

• V3 – (…, 3) shaped array with the third eigenvector

• L1 – (…, ) shaped array with the first eigenvalue

• L2 – (…, ) shaped array with the second eigenvalue

• L3 – (…, ) shaped array with the third eigenvalue

Returns:

(…, 6) array with the unique components sorted like Dxx, Dxy, Dxz, Dyy, Dyz, Dzz

fsl.data.dtifit.componentsToEigendecomposition(components)

Decomposes diffusion tensor defined by its 6 unique components

Parameters:

components – (…, 6) array-like with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz

Returns:

Tuple containing the eigenvectors and eigenvalues (V1, V2, V3, L1, L2, L3)

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(*args, **kwargs)

Bases: 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.

__init__(path)

Create a DTIFitTensor.

Parameters:

path – A path to a dtifit directory.

V1()

V2()

V3()

L1()

L2()

L3()

__annotations__ = {}

__module__ = 'fsl.data.dtifit'