2 November 2001 Three-dimensional optical diffusion tomography using iterative coordinate descent optimization
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Abstract
We demonstrate accurate and efficient three-dimensional optical diffusion imaging using simulated noisy data from a set of measurements at a single modulation frequency. A Bayesian framework provides for prior model conditioning, and a dual-step cost function optimization allows sequential estimation of the data noise variance and the image.
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Adam B. Milstein, Seungseok Oh, Kevin J. Webb, Charles A. Bouman, Rick P. Millane, "Three-dimensional optical diffusion tomography using iterative coordinate descent optimization", Proc. SPIE 4431, Photon Migration, Optical Coherence Tomography, and Microscopy, (2 November 2001); doi: 10.1117/12.447409; https://doi.org/10.1117/12.447409
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