Approximate Poisson likelihoods for simple optimization in MAP tomographic estimation

Jean-Baptiste Thibault; Ken D. Sauer; Charles A. Bouman

doi:10.1117/12.351311

25 June 1999 Approximate Poisson likelihoods for simple optimization in MAP tomographic estimation

Jean-Baptiste Thibault, Ken D. Sauer, Charles A. Bouman

Proceedings Volume 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems; (1999) https://doi.org/10.1117/12.351311
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

Emission Computed Tomography (ECT) is widely applied in medical diagnostic imaging, especially to determine physiological function. The available set of measurements is,however, often incomplete and corrupted, and the quality of image reconstruction is enhanced by the computation of a statistically optimal estimate. We present here a numerical method of ECT image reconstruction based on a Taylor series quadratic approximation to the usual Poison log-likelihood function. The quadratic approximation yields simplification in understanding and manipulating Poisson models. We introduce an algorithm similar to global Newton methods which updates the point of expansion a limited number of time sand we give quantitative measures of the accuracy of the reconstruction. The result show little difference in quality from those obtained with the exact Poisson model.

Citation Download Citation

Jean-Baptiste Thibault, Ken D. Sauer, and Charles A. Bouman "Approximate Poisson likelihoods for simple optimization in MAP tomographic estimation", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); https://doi.org/10.1117/12.351311

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