usage: __main__.py [-h] [--out_fd OUT_FD] [--out_fs OUT_FS] [--out_ff OUT_FF]
[--not_all] [--mask MASK]
[--nbr_integration_steps NBR_INTEGRATION_STEPS]
[-v [{DEBUG,INFO,WARNING}]] [--processes NBR] [-f]
in_bingham
Script to compute fODF lobe-specific metrics derived from a Bingham
distribution fit, as described in [1]. Resulting metrics are fiber density
(FD), fiber spread (FS) and fiber fraction (FF) [2].
The Bingham coefficients volume comes from scil_fodf_to_bingham.py.
A lobe's FD is the integral of the Bingham function on the sphere. It
represents the density of fibers going through a given voxel for a given
fODF lobe (fixel). A lobe's FS is the ratio of its FD on its maximum AFD. It
is at its minimum for a sharp lobe and at its maximum for a wide lobe. A lobe's
FF is the ratio of its FD on the total FD in the voxel.
Using 12 threads, the execution takes 10 minutes for FD estimation for a brain
with 1mm isotropic resolution. Other metrics take less than a second.
Formerly: scil_compute_lobe_specific_fodf_metrics.py
positional arguments:
in_bingham Input Bingham nifti image.
options:
-h, --help show this help message and exit
--out_fd OUT_FD Path to output fiber density. [fd.nii.gz]
--out_fs OUT_FS Path to output fiber spread. [fs.nii.gz]
--out_ff OUT_FF Path to fiber fraction file. [ff.nii.gz]
--not_all Do not compute all metrics. Then, please provide the output paths of the files you need.
--mask MASK Optional mask image. Only voxels inside the mask are computed.
--nbr_integration_steps NBR_INTEGRATION_STEPS
Number of integration steps along the theta axis for fiber density estimation. [50]
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
--processes NBR Number of sub-processes to start.
Default: [1]
-f Force overwriting of the output files.
[1] T. W. Riffert, J. Schreiber, A. Anwander, and T. R. Knösche, “Beyond
fractional anisotropy: Extraction of bundle-specific structural metrics
from crossing fiber models,” NeuroImage, vol. 100, pp. 176-191, Oct. 2014,
doi: 10.1016/j.neuroimage.2014.06.015.
[2] J. Schreiber, T. Riffert, A. Anwander, and T. R. Knösche, “Plausibility
Tracking: A method to evaluate anatomical connectivity and microstructural
properties along fiber pathways,” NeuroImage, vol. 90, pp. 163-178, Apr.
2014, doi: 10.1016/j.neuroimage.2014.01.002.