期刊
NEUROIMAGE
卷 146, 期 -, 页码 507-517出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2016.10.040
关键词
Diffusion-weighted imaging; Factorization; Spherical deconvolution; Multi-tissue model; Multi-shell HARDI; Blind source separation
资金
- Agency for Innovation by Science and Technology (IWT) [121013]
- KU Leuven Concerted Research Action [GOA/11/006]
- Human Connectome Project, WU-Minn Consortium - 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
Diffusion-weighted imaging (DWI) facilitates probing neural tissue structure non-invasively by measuring its hindrance to water diffusion. Analysis of DWI is typically based on generative signal models for given tissue geometry and microstructural properties. In this work, we generalize multi-tissue spherical deconvolution to a blind source separation problem under convexity and nonnegativity constraints. This spherical factorization approach decomposes multi-shell DWI data, represented in the basis of spherical harmonics, into tissue-specific orientation distribution functions and corresponding response functions, without assuming the latter as known thus fully unsupervised. In healthy human brain data, the resulting components are associated with white matter fibres, grey matter, and cerebrospinal fluid. The factorization results are on par with state-of-the-art supervised methods, as demonstrated also in Monte-Carlo simulations evaluating accuracy and precision of the estimated response functions and orientation distribution functions of each component. In animal data and in the presence of oedema, the proposed factorization is able to recover unseen tissue structure, solely relying on DWI. As such, our method broadens the applicability of spherical deconvolution techniques to exploratory analysis of tissue structure in data where priors are uncertain or hard to define.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据