`AFQ.models.dti`#

Module Contents#

`fit_dti`(data_files, bval_files, bvec_files[, mask, ...])	Fit the DTI model using default settings, save files with derived maps.
`predict`(params_file, gtab[, S0_file, out_dir])	Create a signal prediction from DTI params.

AFQ.models.dti.fit_dti(data_files, bval_files, bvec_files, mask=None, out_dir=None, file_prefix=None, b0_threshold=50)[source]#

Fit the DTI model using default settings, save files with derived maps.

Parameters

data_filesstr or list: Files containing DWI data. If this is a str, that’s the full path to a single file. If it’s a list, each entry is a full path.
bval_filesstr or list: Equivalent to data_files.
bvec_filesstr or list: Equivalent to data_files.
maskndarray, optional: Binary mask, set to True or 1 in voxels to be processed. Default: Process all voxels.
out_dirstr, optional: A full path to a directory to store the maps that get computed. Default: maps get stored in the same directory as the last DWI file in data_files.
b0_thresholdfloat

Returns

file_pathsdict: A dict with the derived maps that were computed and full-paths to the files containing these maps.

Notes

Maps that are calculated: FA, MD, AD, RD

AFQ.models.dti.predict(params_file, gtab, S0_file=None, out_dir=None)[source]#

Create a signal prediction from DTI params.

Parameters

params_filestr: Full path to a file with parameters saved from a DKI fit
gtabGradientTable object: The gradient table to predict for
S0_filestr: Full path to a nifti file that contains S0 measurements to incorporate into the prediction. If the file contains 4D data, the volumes that contain the S0 data must be the same as the gtab.b0s_mask.

Returns