Maximum Likelihood Estimators of Brain White Matter Microstructure

Olivier Commowick 1, * Aymeric Stamm 2, 3 Simone Vantini 3 Simon Warfield 2
* Corresponding author
1 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 : The microstructure of the brain white matter is not visible to the naked eye but would be of invaluable help to the clinician in the diagnosis and treatment of many brain pathologies. Diffusion MRI is an in-vivo non invasive imaging technique that probes the cyto-architecture of the white matter through the diffusion of water. However, diffusion MRI is limited in resolution, which makes forward models of the diffusion at the voxel level rather complex. In this paper, we provide a statistical framework for recovering the maximum-likelihood estimators of the parameters of mixture models of the diffusion. We calibrate different methods on simulated data to guarantee convergence to the maximum likelihood and show that profile likelihood maximization using variable projection together with a Levenberg-Marquardt algorithm with analytic Jacobian is the most efficient method to obtain the MLE.
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Olivier Commowick, Aymeric Stamm, Simone Vantini, Simon Warfield. Maximum Likelihood Estimators of Brain White Matter Microstructure. 48th Scientific Meeting of the Italian Statistical Society, Jun 2016, Salerno, Italy. ⟨inserm-01347200⟩

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