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Comprehensive Maximum Likelihood Estimation of Diffusion Compartment Models Towards Reliable Mapping of Brain Microstructure

  • Aymeric StammEmail author
  • Olivier Commowick
  • Simon K. Warfield
  • S. Vantini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Diffusion MRI is a key in-vivo non invasive imaging capability that can probe the microstructure of the brain. However, its limited resolution requires complex voxelwise generative models of the diffusion. Diffusion Compartment (DC) models divide the voxel into smaller compartments in which diffusion is homogeneous. We present a comprehensive framework for maximum likelihood estimation (MLE) of such models that jointly features ML estimators of (i) the baseline MR signal, (ii) the noise variance, (iii) compartment proportions, and (iv) diffusion-related parameters. ML estimators are key to providing reliable mapping of brain microstructure as they are asymptotically unbiased and of minimal variance. We compare our algorithm (which efficiently exploits analytical properties of MLE) to alternative implementations and a state-of-the-art strategy. Simulation results show that our approach offers the best reduction in computational burden while guaranteeing convergence of numerical estimators to the MLE. In-vivo results also reveal remarkably reliable microstructure mapping in areas as complex as the centrum semi-ovale. Our ML framework accommodates any DC model and is available freely for multi-tensor models as part of the ANIMA software (https://github.com/Inria-Visages/Anima-Public/wiki).

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© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Aymeric Stamm
    • 1
    • 2
    Email author
  • Olivier Commowick
    • 3
  • Simon K. Warfield
    • 2
  • S. Vantini
    • 1
  1. 1.MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
  2. 2.CRL, Harvard Medical SchoolBoston Children’s HospitalBostonUSA
  3. 3.VISAGES, INSERM U746, CNRS UMR6074, INRIA, University of Rennes IRennesFrance

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