Paper
2 November 2001 Three-dimensional optical diffusion tomography using iterative coordinate descent optimization
Author Affiliations +
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adam B. Milstein, Seungseok Oh, Kevin J. Webb, Charles A. Bouman, and 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); https://doi.org/10.1117/12.447409
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KEYWORDS
Diffusion

Data modeling

3D modeling

Tomography

Optical tomography

Optimization (mathematics)

Sensors

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