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 Simon K. Warfield 1
* Auteur correspondant
3 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Diffusion images are known to be corrupted with a non-central chi (NCC)-distributed noise [1]. There has been a number of proposed image denoising methods that account for this particular noise distribution [1,2,3]. However, to the best of our knowledge, no study was performed to assess the influence of the noise model in the context of diffusion model estimation as was suggested in [4]. In particular, multi-compartment models [5] are an appealing class of models to describe the white matter microstructure but require the optimal number of compartments to be known a priori. Its estimation is no easy task since more complex models will always better fit the data, which is known as over-fitting. However, MCM estimation in the literature is performed assuming a Gaussian-distributed noise [5,6]. In this preliminary study, we aim at showing that using the appropriate NCC distribution for modelling the noise model reduces significantly the over-fitting, which could be helpful for unravelling model selection issues and obtaining better model parameter estimates.
Type de document :
Communication dans un congrès
ISMRM, May 2014, Milan, Italy. pp.2623, 2014
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http://www.hal.inserm.fr/inserm-00993964
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  • HAL Id : inserm-00993964, version 1

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Aymeric Stamm, Benoit Scherrer, Stefano Baraldo, Olivier Commowick, Simon K. Warfield. Non-central chi estimation of multi-compartment models improves model selection by reducing overfitting. ISMRM, May 2014, Milan, Italy. pp.2623, 2014. 〈inserm-00993964〉

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