Diffuse Optical Tomography (DOT) image reconstruction is a challenging 3D problem with a relatively large number of unknowns. DOT poses a typical ill-posed problem usually plagued by under-determination, which complicates the inverse problem. Conventional image reconstruction algorithms can not provide high spatial resolution and may become computationally expensive and unreliable especially in the presence of noise. In this work, we extend our previous formulation for the 3D inverse DOT problem, where we focus to improve the spatial resolution and quantitative accuracy of 3D DOT images by using anatomical a priori information, which is specific to the medium of interest. Maximum A Posteriori (MAP) estimate of the image is formed based on the formulation of the image’s probability density function, which is extracted from the available a priori anatomical information. An “alternating minimization” algorithm, which sequentially updates the unknown parameters, is used to solve the resulting optimization problem. Proposed method is evaluated in a 3D simulation experiment. Results demonstrate that the proposed method leads to significantly improved spatial resolution, quantitative accuracy and faster convergence than standard and regularized least squares solutions even in the presence of noise. As a result, the approach demonstrated in this paper both addresses the ill-posedness and balances the computation complexity vs. image quality trade-off in the 3D DOT inverse problem.

© 2003 SPIE

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