Multigrid Bayesian methods for optical diffusion tomography

Rick P. Millane; Jong Chul Ye; Charles A. Bouman; Kevin J. Webb

doi:10.1117/12.409282

16 November 2000 Multigrid Bayesian methods for optical diffusion tomography

Rick P. Millane, Jong Chul Ye, Charles A. Bouman, Kevin J. Webb

Proceedings Volume 4123, Image Reconstruction from Incomplete Data; (2000) https://doi.org/10.1117/12.409282
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

Optical diffusion imaging is a new imaging modality that promises great potential in applications such as medical imaging, environmental sensing and nondestructive testing. It presents a difficult nonlinear image reconstruction problem however. An inversion algorithm is formulated in Bayesian framework, and an efficient optimization technique that uses iterative coordinate descent is presented. A general multigrid optimization technique for nonlinear image reconstruction problems is developed and applied to the optical diffusion imaging problem. Numerical results show that this approach improves the quality of reconstructions and dramatically decreases computation times.

Citation Download Citation

Rick P. Millane, Jong Chul Ye, Charles A. Bouman, and Kevin J. Webb "Multigrid Bayesian methods for optical diffusion tomography", Proc. SPIE 4123, Image Reconstruction from Incomplete Data, (16 November 2000); https://doi.org/10.1117/12.409282

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