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Abstract #5335

The importance of constraints and spherical sampling in diffusion MRI

Tom Dela Haije1 and Aasa Feragen1

1Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

In this work we analyze the incidence of voxels with physically impossible model parameters, reconstructed from diffusion-weighted data that is acquired using different sampling schemes. Our results show that for cumulants up to order $$$4$$$ constrained least squares can be used to compute a reliable reconstruction of the cumulant expansion of the signal from realistic acquisitions, with spherical sampling producing fewer unsatisfied model constraints compared to space-filling sampling. Voxels where reconstruction is likely to fail are shown to be consistently localized near the white matter-gray matter interface and in deep brain structures.

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