Abstract
A reconstruction algorithm for three-dimensional optical diffusion tomography is presented that uses Bayesian regularization and iterative coordinate descent optimization incorporating estimation of source-detector coupling coefficients. Simulations show that this is an accurate and efficient approach.
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