usage: __main__.py [-h] [--length_weighting] [--processes NBR]
[--sh_basis {descoteaux07,tournier07,descoteaux07_legacy,tournier07_legacy}]
[-v [{DEBUG,INFO,WARNING}]] [-f]
in_hdf5 in_fodf out_hdf5
Compute the mean Apparent Fiber Density (AFD) and mean Radial fODF (radfODF)
maps for every connections within a hdf5 (.h5) file.
This is the "real" fixel-based fODF amplitude along every streamline
of each connection, averaged at every voxel.
Please use a hdf5 (.h5) file containing decomposed connections
Formerly: scil_compute_fixel_afd_from_hdf5.py
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Reference:
[1] Raffelt, D., Tournier, JD., Rose, S., Ridgway, GR., Henderson, R.,Crozier,
S., Salvado, O., & Connelly, A. (2012). Apparent Fibre Density: a novel
measure for the analysis of diffusion-weighted magnetic resonance images.
NeuroImage, 59(4), 3976--3994.
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positional arguments:
in_hdf5 HDF5 filename (.h5) containing decomposed connections.
in_fodf Path of the fODF volume in spherical harmonics (SH).
out_hdf5 Path of the output HDF5 filenames (.h5).
options:
-h, --help show this help message and exit
--length_weighting If set, will weigh the AFD values according to segment lengths. [False]
--processes NBR Number of sub-processes to start.
Default: [1]
--sh_basis {descoteaux07,tournier07,descoteaux07_legacy,tournier07_legacy}
Spherical harmonics basis used for the SH coefficients.
Must be either descoteaux07', 'tournier07',
'descoteaux07_legacy' or 'tournier07_legacy' [['descoteaux07_legacy']]:
'descoteaux07' : SH basis from the Descoteaux et al.
MRM 2007 paper
'tournier07' : SH basis from the new Tournier et al.
NeuroImage 2019 paper, as in MRtrix 3.
'descoteaux07_legacy': SH basis from the legacy Dipy implementation
of the Descoteaux et al. MRM 2007 paper
'tournier07_legacy' : SH basis from the legacy Tournier et al.
NeuroImage 2007 paper.
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
-f Force overwriting of the output files.
Scilpy version: 2.0.2