Nonlinear multigrid algorithms for Bayesian optical diffusion tomography

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Jong Chul Ye, Charles A. Bouman, Kevin J. Webb, Rick P. Millane

Published: 2001, Last Modified: 05 Mar 2026IEEE Trans. Image Process. 2001EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Optical diffusion tomography is a technique for imaging a highly scattering medium using measurements of transmitted modulated light. Reconstruction of the spatial distribution of the optical properties of the medium from such data is a difficult nonlinear inverse problem. Bayesian approaches are effective, but are computationally expensive, especially for three-dimensional (3-D) imaging. This paper presents a general nonlinear multigrid optimization technique suitable for reducing the computational burden in a range of nonquadratic optimization problems. This multigrid method is applied to compute the maximum a posteriori (MAP) estimate of the reconstructed image in the optical diffusion tomography problem. The proposed multigrid approach both dramatically reduces the required computation and improves the reconstructed image quality.