Abstract #2623
Non central chi estimation of multi-compartment models improves model selection by reducing overfitting
Aymeric Stamm 1 , Benoit Scherrer 1 , Stefano Baraldo 2 , Olivier Commowick 3 , and Simon Warfield 1
1
Computational Radiology Laboratory, Boston
Children's Hospital, Boston, MA, United States,
2
MOX,
Politecnico di Milano, Milan, Italy,
3
VISAGES,
INRIA, Rennes, France, Metropolitan
Noise in diffusion MRI is known to be characterized by a
non-central chi distribution. Many denoising methods
have accounted for this but, for the estimation of
diffusion models, the noise is most of the time still
approximated by a Gaussian distribution. In this
abstract, we examine the impact of this approximation to
determine the optimal number of fascicles required for
the estimation of multi-compartment models. We show that
performing the models' estimation within a non-central
chi framework significantly reduces over-fitting thus
yielding a more reliable selection of the optimal number
of fascicles.
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