Classes and functions for generalized q-sampling
Implements Generalized Q-Sampling
Generates a model-free description for every voxel that can be used from simple to very complicated configurations like quintuple crossings if your datasets support them.
You can use this class for every kind of DWI image but it will perform much better when you have a balanced sampling scheme.
Implements equation [9] from Generalized Q-Sampling as described in Fang-Cheng Yeh, Van J. Wedeen, Wen-Yih Isaac Tseng. Generalized Q-Sampling Imaging. IEEE TMI, 2010.
Parameters : | data : array,
bvals : array,
gradients : array,
Lambda : float,
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Notes
In order to reconstruct the spin distribution function a nice symmetric evenly distributed sphere is provided using 362 or 642 points. This is usually sufficient for most of the datasets.
Methods
ind | |
npa | |
odf | |
qa |
indices on the sampling sphere
non-parametric anisotropy
Nimmo-Smith et. al ISMRM 2011
spin density orientation distribution function
Parameters : | s : array, shape(D),
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Returns : | odf : array, shape(len(odf_vertices)),
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quantitative anisotropy
finds the ‘vertices’ in the equatorial zone conjugate to ‘pole’ with width half ‘width’ degrees
find ‘vertices’ within the cone of ‘width’ degrees around ‘pole’
finds the ‘vertices’ in the equatorial band around the ‘pole’ of radius ‘width’ degrees
maps a 3-vector into the z-upper hemisphere