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Neuroimage. 2012 Feb 1;59(3):2241-54. doi: 10.1016/j.neuroimage.2011.09.081. Epub 2011 Oct 7.

Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison.

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  • 1Centre for Medical Image Computing, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK. E.Panagiotaki@cs.ucl.ac.uk

Abstract

This paper aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a taxonomy of multi-compartment models of white matter from combinations of simple models for the intra- and the extra-axonal spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of imaging parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and compare the different models. The study examines a total of 47 analytic models, including several well-used models from the literature, which we place within the taxonomy. The results show that models that incorporate intra-axonal restriction, such as ball and stick or CHARMED, generally explain the data better than those that do not, such as the DT or the biexponential models. However, three-compartment models which account for restriction parallel to the axons and incorporate pore size explain the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-axonal space with a cylindrical intra-axonal component of single radius and a third spherical compartment of non-zero radius. We also measure the stability of the non-zero radius intra-axonal models and find that single radius intra-axonal models are more stable than gamma distributed radii models with similar fitting performance.

Copyright © 2011 Elsevier Inc. All rights reserved.

PMID:
22001791
[PubMed - indexed for MEDLINE]
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