title: Variational inference with ARD prior for NIRS diffuse optical tomography.
abstract: Diffuse optical tomography (DOT) reconstructs 3-D tomographic images of brain activities from observations by near-infrared spectroscopy (NIRS) that is formulated as an ill-posed inverse problem. This brief presents a method for NIRS DOT based on a hierarchical Bayesian approach introducing the automatic relevance determination prior and the variational Bayes technique. Although the sparseness of the estimation strongly depends on the hyperparameters, in general, our method has less dependency on the hyperparameters. We confirm through numerical experiments that a schematic phase diagram of sparseness with respect to the hyperparameters has two regions: in one region hyperparameters give sparse solutions and in the other they give dense ones. The experimental results are supported by our theoretical analyses in simple cases.
authors.forename: Atsushi
authors.initials: A
authors.lastname: Miyamoto
authors.forename: Kazuho
authors.initials: K
authors.lastname: Watanabe
authors.forename: Kazushi
authors.initials: K
authors.lastname: Ikeda
authors.forename: Masa-Aki
authors.initials: MA
authors.lastname: Sato
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000378:metabolism*; D003198:Computer Simulation; D006454:Hemoglobins / Q000378:metabolism*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D019265:Spectroscopy, Near-Infrared; D041622:Tomography, Optical
chemical_list: D006454:Hemoglobins