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Abstract #2629

Fast and robust detection of the optimal number of fascicles in diffusion images using model averaging theory

Aymeric Stamm 1 , Benoit Scherrer 1 , Olivier Commowick 2 , Christian Barillot 3 , and Simon Warfield 1

1 Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, United States, 2 VISAGES, INRIA, Rennes, France, Metropolitan, 3 VISAGES, CNRS, Rennes, France, Metropolitan

It is well known that the white matter has a complex architecture composed mainly of axon bundles or fascicles and glial cells. Fascicles cross in most parts of the white matter and multi-compartment models have been devised to study this complex microstructure. These models require that the number of compartments is known a priori, which is not the case in practice. In particular, determining the number of fascicles is difficult. It can however be reliably estimated from the generalization error at the cost of huge computational time. We propose a novel approach that relies on model averaging theory and generates the same results as the generalization error in a dramatically reduced computational time.

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