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Proceedings Paper

Three-dimensional optical diffusion tomography using iterative coordinate descent optimization
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Paper 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.

Paper Details

Date Published: 2 November 2001
PDF: 8 pages
Proc. SPIE 4431, Photon Migration, Optical Coherence Tomography, and Microscopy, (2 November 2001); doi: 10.1117/12.447409
Show Author Affiliations
Adam B. Milstein, Purdue Univ. (United States)
Seungseok Oh, Purdue Univ. (United States)
Kevin J. Webb, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
Rick P. Millane, Purdue Univ. (New Zealand)


Published in SPIE Proceedings Vol. 4431:
Photon Migration, Optical Coherence Tomography, and Microscopy
Stefan Andersson-Engels; Michael F. Kaschke, Editor(s)

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