Adaptive iterative thresholding algorithms for magnetoencephalography (MEG)

[摘要] We provide fast and accurate adaptive algorithms for the spatial resolution of current densities in MEG. We assume that vector components of the current densities possess a sparse expansion with respect to preassigned wavelets. Additionally, different components may also exhibit common sparsity patterns. We model MEG as all inverse problem with joint sparsity constraints, promoting the coupling of non-vanishing components. We show how to compute solutions of the MEG linear inverse problem by iterative thresholded Landweber schemes. The resulting adaptive scheme is fast, robust, and significantly Outperforms the classical Tikhonov regularization in resolving sparse current densities. Numerical examples are included. (C) 2007 Elsevier B.V. All rights reserved.

[发布日期] 2008-11-15

[效力级别]  Proceedings Paper

[关键词] Magnetoencephalography;Inverse problems;Iterative thresholding;Adaptive algorithms;Matrix compression;Wavelets