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Proceedings Paper

Bayesian optical diffusion imaging
Author(s): Jong Chul Ye; Charles A. Bouman; Kevin J. Webb; Rick P. Millane
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Paper Abstract

Frequency-domain diffusion imaging is a new imaging modality which uses the magnitude and phase of modulated light propagation through a highly scattering medium to reconstruct an image of the scattering and/or the absorption coefficient in the medium. In this paper, the inversion algorithm is formulated in a Bayesian framework and an efficient optimization technique is presented for calculating the maximum a posteriori image. Numerical result show that the Bayesian framework with the new optimization scheme out-performs conventional approaches in both speed and reconstruction quality.

Paper Details

Date Published: 25 June 1999
PDF: 12 pages
Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351329
Show Author Affiliations
Jong Chul Ye, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
Kevin J. Webb, Purdue Univ. (United States)
Rick P. Millane, Purdue Univ. (New Zealand)

Published in SPIE Proceedings Vol. 3816:
Mathematical Modeling, Bayesian Estimation, and Inverse Problems
Françoise J. Prêteux; Ali Mohammad-Djafari; Edward R. Dougherty, Editor(s)

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