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Abstract #0362

Super-Resolving Patches in Diffusion MRI Using Canonical Fibre Configurations

Simon Jeremy Damion Prince1, Daniel C. Alexander1

1Computer Science, University College London, London, UK

We aim to improve the spatial resolution of fibre-orientation estimates in diffusion MRI by learning a prior over 3x3x3 voxel patches that assumes self-similarity across scale. In training we align and cluster patches of fibre orientations from a larger scale using a mixture of Watsons model. The resulting canonical fibre configurations describe homogenous regions, bending, fanning etc. To super-resolve, we find the canonical patch and mean orientation, diffusivity and volume fraction that best describe the voxel measurements. We compare our results to nearest-neighbour interpolation and demonstrate that it is possible to successfully determine sub-voxel structure.