usage: __main__.py [-h] [--out_sym OUT_SYM]
[--sh_basis {descoteaux07,tournier07,descoteaux07_legacy,tournier07_legacy}]
[--sphere {repulsion100,repulsion200,repulsion724,symmetric362,symmetric642,symmetric724}]
[--method {unified,cosine}] [--sigma_spatial SIGMA_SPATIAL]
[--sigma_align SIGMA_ALIGN] [--sigma_range SIGMA_RANGE]
[--sigma_angle SIGMA_ANGLE] [--disable_spatial]
[--disable_align] [--disable_range] [--include_center]
[--win_hwidth WIN_HWIDTH] [--sharpness SHARPNESS]
[--device {cpu,gpu}] [--use_opencl]
[--patch_size PATCH_SIZE] [-v [{DEBUG,INFO,WARNING}]] [-f]
in_sh out_sh
Script to estimate asymmetric ODFs (aODFs) from a spherical harmonics image.
Two methods are available:
* Unified filtering [1] combines four asymmetric filtering methods into
a single equation and relies on a combination of four gaussian filters.
* Cosine filtering [2] is a simpler implementation using cosine distance
for assigning weights to neighbours.
Unified filtering can be accelerated using OpenCL with the option --use_opencl.
Make sure you have pyopencl installed before using this option. By default, the
OpenCL program will run on the cpu. To use a gpu instead, also specify the
option --device gpu.
positional arguments:
in_sh Path to the input file.
out_sh File name for averaged signal.
options:
-h, --help show this help message and exit
--out_sym OUT_SYM Name of optional symmetric output. [None]
--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.
--sphere {repulsion100,repulsion200,repulsion724,symmetric362,symmetric642,symmetric724}
Sphere used for the SH to SF projection. [repulsion200]
--method {unified,cosine}
Method for estimating asymmetric ODFs [unified].
One of:
'unified': Unified filtering [1].
'cosine' : Cosine-based filtering [2].
--device {cpu,gpu} Device to use for execution. [cpu]
--use_opencl Accelerate code using OpenCL (requires pyopencl
and a working OpenCL implementation).
--patch_size PATCH_SIZE
OpenCL patch size. [40]
-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.
Shared filter arguments:
--sigma_spatial SIGMA_SPATIAL
Standard deviation for spatial distance. [1.0]
Unified filter arguments:
--sigma_align SIGMA_ALIGN
Standard deviation for alignment filter. [0.8]
--sigma_range SIGMA_RANGE
Standard deviation for range filter
*relative to SF range of image*. [0.2]
--sigma_angle SIGMA_ANGLE
Standard deviation for angular filter
(disabled by default).
--disable_spatial Disable spatial filtering.
--disable_align Disable alignment filtering.
--disable_range Disable range filtering.
--include_center Include center voxel in neighourhood.
--win_hwidth WIN_HWIDTH
Filtering window half-width. Defaults to 3*sigma_spatial.
Cosine filter arguments:
--sharpness SHARPNESS
Specify sharpness factor to use for
weighted average. [1.0]
[1] Poirier and Descoteaux, 2024, "A Unified Filtering Method for Estimating
Asymmetric Orientation Distribution Functions", Neuroimage, vol. 287,
https://doi.org/10.1016/j.neuroimage.2024.120516
[2] Poirier et al, 2021, "Investigating the Occurrence of Asymmetric Patterns
in White Matter Fiber Orientation Distribution Functions", ISMRM 2021
(abstract 0865)