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

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• 作者：Enrico Kaden^a ; ^{enrico.kaden@gmail.com} ; Frithjof Kruggel^b ; ^{fkruggel@uci.edu}
• 关键词：Spherical deconvolution ; Lebesgue decomposition ; Dirichlet process mixture ; Reversible jump MCMC ; Diffusion MR imaging
• 刊名：Medical Image Analysis
• 出版年：2012
• 期刊代码：90_13618415
• 类别：cp
• 出版时间：May, 2012
• 卷：16
• 期：4
• 页码：876-888
• 文件大小：2572 K

摘要

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.