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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