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Med Image Anal. 2012 May;16(4):876-88. doi: 10.1016/ Epub 2012 Feb 1.

Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images.

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  • 1Department of Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany.


Diffusion MR imaging provides a unique tool to probe the microgeometry of nervous tissue and to explore the wiring diagram of the neural connections noninvasively. Generally, a forward model is established to map the intra-voxel fiber architecture onto the observable diffusion signals, which is reformulated in this article by adopting a measure-theoretic approach. However, the inverse problem, i.e., the spherical deconvolution of the fiber orientation density from noisy MR measurements, is ill-posed. We propose a nonparametric representation of the tangential distribution of the nerve fibers in terms of a Dirichlet process mixture. Given a second-order approximation of the impulse response of a fiber segment, the specified problem is solved by Bayesian statistics under a Rician noise model, using an adaptive reversible jump Markov chain Monte Carlo sampler. The density estimation framework is demonstrated by various experiments with a diffusion MR dataset featuring high angular resolution, uncovering the fiber orientation field in the cerebral white matter of the living human brain.

Copyright © 2012 Elsevier B.V. All rights reserved.

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