Application of a probabilistic double-fibre structure model to diffusion-weighted MR images of the human brain

Abstract A Markov chain Monte Carlo (MCMC) algorithm has been reported which is capable of determining the probabilistic orientation of two-fibre populations from high angular resolution diffusion-weighted data (HARDI). We present and critically discuss the application of this algorithm to in vivo h...

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Bibliographic Details
Published in:Magnetic resonance imaging 2008-02, Vol.26 (2), p.236-245
Main Authors: Hosey, Tim P, Harding, Sally G, Carpenter, T. Adrian, Ansorge, Richard E, Williams, Guy B
Format: Article
Language:English
Subjects:
Algorithms
Anisotropy
Bayes Theorem
Brain Mapping - methods
Diffusion Magnetic Resonance Imaging - methods
Diffusion MRI
Humans
Markov chain Monte Carlo
Markov Chains
Monte Carlo Method
Nerve Fibers, Myelinated - physiology
Probability
Radiology
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Summary:Abstract A Markov chain Monte Carlo (MCMC) algorithm has been reported which is capable of determining the probabilistic orientation of two-fibre populations from high angular resolution diffusion-weighted data (HARDI). We present and critically discuss the application of this algorithm to in vivo human datasets acquired in clinically realistic times. We show that by appropriate model selection areas of multiple fibre populations can be identified that correspond with those predicted from known anatomy. Quantitative maps of fibre orientation probability are derived and shown for one- and two-fibre models of neural architecture. Fibre crossings in the pons, the internal capsule and the corona radiata are shown. In addition, we demonstrate that the relative proportion of anisotropic signal may be a more appropriate measure of anisotropy than summary measures derived from the tensor model such as fractional anisotropy in areas with multi-fibre populations.
ISSN:0730-725X
1873-5894
DOI:10.1016/j.mri.2007.07.002