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Abstract #0349

Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images

Ben Jeurissen 1 , Jacques-Donald Tournier 2,3 , and Jan Sijbers 1

1 iMinds-Vision Lab, Dept. of Physics, University of Antwerp, Antwerp, Belgium, 2 Centre for the Developing Brain, King's College London, London, United Kingdom, 3 Dept. of Biomedical Engineering, King's College London, London, United Kingdom

Advanced processing of diffusion-weighted (DW) MRI often relies on properly aligned anatomical scans and their segmentations to identify specific tissue types, which can prove challenging due to EPI distortions. We introduce a fast, data-driven method for tissue-type segmentation of multi-shell DW MRI images based on non-negative matrix factorization. Experiments show that our method provides good quality segmentation of CSF, GM and WM, straight from the raw DW and without any spatial priors. We show that the proposed technique can be used to estimate response functions for multi-shell, multi-tissue constrained spherical deconvolution, removing the dependency of this technique on anatomical scans.

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