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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