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Proceedings Paper

Hybrid approach to Bayesian image reconstruction
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Paper Abstract

The approximate extended Kalman filter (AEKF) has been suggested as an appropriate inverse method for reconstructing fluorescent properties in large tissue samples from frequency domain data containing measurement error. The AEKF is an “optimal” estimator, in that it seeks to minimize the predicted error variances of the estimated optical properties in relation to measurement and system errors. However, due to non-linearities in the recursive estimation process, the updates are actually suboptimal. Furthermore, the computational overhead is large for the full AEKF algorithm when applied to large datasets. In this contribution we developed three hybrid forms of the AEKF algorithm that may improve the performance in frequency domain fluorescence tomography. Numerical results of image reconstruction from actual frequency domain emission data show that one hybrid form of the AEKF outperforms the traditional full AEKF in both image quality and computational efficiency for the two cases tested.

Paper Details

Date Published: 29 July 2003
PDF: 9 pages
Proc. SPIE 4955, Optical Tomography and Spectroscopy of Tissue V, (29 July 2003); doi: 10.1117/12.478184
Show Author Affiliations
Chaoyang Zhang, Univ. of Vermont (United States)
Margaret J. Eppstein, Univ. of Vermont (United States)
Anuradha Godavarty, Texas A&M Univ. (United States)
Eva Marie Sevick-Muraca, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 4955:
Optical Tomography and Spectroscopy of Tissue V
Britton Chance; Robert R. Alfano; Bruce J. Tromberg; Mamoru Tamura; Eva M. Sevick-Muraca, Editor(s)

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