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dipy.reconst.maskedview

dipy.reconst.gqi

Classes and functions for generalized q-sampling

dipy.reconst.gqi.equatorial_zone_vertices(vertices, pole, width=5)

finds the ‘vertices’ in the equatorial zone conjugate to ‘pole’ with width half ‘width’ degrees

dipy.reconst.gqi.normalize_qa(qa, max_qa=None)

Normalize quantitative anisotropy.

Used mostly with GQI rather than GQI2.

Parameters :

qa : array, shape (X, Y, Z, N)

where N is the maximum number of peaks stored

max_qa : float,

maximum qa value. Usually found in the CSF (corticospinal fluid).

Returns :

nqa : array, shape (x, Y, Z, N)

normalized quantitative anisotropy

Notes

Normalized quantitative anisotropy has the very useful property to be very small near gray matter and background areas. Therefore, it can be used to mask out white matter areas.

dipy.reconst.gqi.npa(self, odf, width=5)

non-parametric anisotropy

Nimmo-Smith et. al ISMRM 2011

dipy.reconst.gqi.patch_vertices(vertices, pole, width)

find ‘vertices’ within the cone of ‘width’ degrees around ‘pole’

dipy.reconst.gqi.polar_zone_vertices(vertices, pole, width=5)

finds the ‘vertices’ in the equatorial band around the ‘pole’ of radius ‘width’ degrees

dipy.reconst.gqi.squared_radial_component(x, tol=0.01)

Part of the GQI2 integral

Eq.8 in the referenced paper by Yeh et al. 2010

dipy.reconst.gqi.upper_hemi_map(v)

maps a 3-vector into the z-upper hemisphere