Non-central chi estimation of multi-compartment models improves model selection by reducing overfitting - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Non-central chi estimation of multi-compartment models improves model selection by reducing overfitting

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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.
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Dates and versions

inserm-00993964 , version 1 (21-05-2014)

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  • HAL Id : inserm-00993964 , version 1

Cite

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. ⟨inserm-00993964⟩
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