Optical diffusion tomography by iterative-coordinate-descent optimization in a Bayesian framework

Frequency-domain diffusion imaging uses the magnitude and phase of modulate d light propagating through a highly scattering medium to reconstruct an im age of the spatially dependent scattering or absorption coefficients in the medium. An inversion algorithm is formulated in a Bayesian framework and a n efficient optimization technique is presented for calculating the maximum a posteriori image. In this framework the data are modeled as a complex Ga ussian random vector with shot-noise statistics, and the unknown image is m odeled as a generalized Gaussian Markov random field. The shot-noise statis tics provide correct weighting for the measurement, and the generalized Gau ssian Markov random field prier enhances the reconstruction quality and ret ains edges in the reconstruction. A localized relaxation algorithm, the ite rative-coordinate-descent algorithm, is employed as a computationally effic ient optimization technique. Numerical results for two-dimensional images s how that the Bayesian framework with the new optimization scheme outperform s conventional approaches in both speed and reconstruction quality. (C) 199 9 Optical Society of America [S0740-3232(99)01410-6].