AFQ.models.asym_filtering#

Functions#

`unified_filtering`(sh_data, sphere[, sh_basis, ...])	Unified asymmetric filtering as described in [1].
`compute_asymmetry_index`(sh_coeffs, mask)	Compute asymmetry index (ASI) [1] from
`compute_odd_power_map`(sh_coeffs, mask)	Compute odd-power map [1] from
`compute_nufid_asym`(sh_coeffs, sphere, csf, mask)	Number of fiber directions (nufid) map [1].

Module Contents#

AFQ.models.asym_filtering.unified_filtering(sh_data, sphere, sh_basis='descoteaux07', is_legacy=True, sigma_spatial=1.0, sigma_align=0.8, sigma_angle=None, rel_sigma_range=0.2, n_threads=None, low_mem=False)[source]#

Unified asymmetric filtering as described in [1].

Parameters:

sh_data: ndarray: SH coefficients image.
sphere: str or DIPY sphere: Name of the DIPY sphere to use for SH to SF projection.
sh_basis: str: SH basis definition used for input and output SH image. One of ‘descoteaux07’ or ‘tournier07’. Default: ‘descoteaux07’.
is_legacy: bool: Whether the legacy SH basis definition should be used. Default: False.
sigma_spatial: float or None: Standard deviation of spatial filter. Can be None to replace by mean filter, in what case win_hwidth must be given.
sigma_align: float or None: Standard deviation of alignment filter. None disables alignment filtering.
sigma_angle: float or None: Standard deviation of the angle filter. None disables angle filtering.
rel_sigma_range: float or None: Standard deviation of the range filter, relative to the range of SF amplitudes. None disables range filtering.
n_threads: int or None: Number of threads to use for numba. If None, uses the number of available threads. Default: None.
low_mem: bool: Whether to use the low-memory version of the filtering. It will be between 50% and 100% slower. Default: False.

References

[1] Poirier and Descoteaux, 2024, “A Unified Filtering Method for: Estimating Asymmetric Orientation Distribution Functions”, Neuroimage, https://doi.org/10.1016/j.neuroimage.2024.120516

AFQ.models.asym_filtering.compute_asymmetry_index(sh_coeffs, mask)[source]#

Compute asymmetry index (ASI) [1] from asymmetric ODF volume expressed in full SH basis.

Parameters:

sh_coeffs: ndarray (x, y, z, ncoeffs): Input spherical harmonics coefficients.
mask: ndarray (x, y, z), bool: Mask inside which ASI should be computed.

Returns:

asi_map: ndarray (x, y, z): Asymmetry index map.

References

[1] S. Cetin Karayumak, E. Özarslan, and G. Unal,: “Asymmetric Orientation Distribution Functions (AODFs) revealing intravoxel geometry in diffusion MRI” Magnetic Resonance Imaging, vol. 49, pp. 145-158, Jun. 2018, doi: https://doi.org/10.1016/j.mri.2018.03.006.

AFQ.models.asym_filtering.compute_odd_power_map(sh_coeffs, mask)[source]#

Compute odd-power map [1] from asymmetric ODF volume expressed in full SH basis.

Parameters:

sh_coeffs: ndarray (x, y, z, ncoeffs): Input spherical harmonics coefficients.
mask: ndarray (x, y, z), bool: Mask inside which odd-power map should be computed.

Returns:

odd_power_map: ndarray (x, y, z): Odd-power map.

References

[1] C. Poirier, E. St-Onge, and M. Descoteaux,: “Investigating the Occurrence of Asymmetric Patterns in White Matter Fiber Orientation Distribution Functions” [Abstract], In: Proc. Intl. Soc. Mag. Reson. Med. 29 (2021), 2021 May 15-20, Vancouver, BC, Abstract number 0865.

AFQ.models.asym_filtering.compute_nufid_asym(sh_coeffs, sphere, csf, mask)[source]#

Number of fiber directions (nufid) map [1].

Parameters:

sh_coeffs: ndarray (x, y, z, ncoeffs): Input spherical harmonics coefficients.
sphere: DIPY sphere: Sphere for SH to SF projection.
csf: ndarray (x, y, z): CSF probability map, used to guess the absolute threshold.
mask: ndarray (x, y, z), bool: Mask inside which ASI should be computed.

References

[1] C. Poirier and M. Descoteaux,: “Filtering Methods for Asymmetric ODFs: Where and How Asymmetry Occurs in the White Matter.” bioRxiv. 2022 Jan 1; 2022.12.18.520881. doi: https://doi.org/10.1101/2022.12.18.520881