Fast Bayesian estimation methods in emission tomography

Alvaro R. De Pierro

doi:10.1117/12.323789

22 September 1998 Fast Bayesian estimation methods in emission tomography

Alvaro R. De Pierro

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323789
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

Since the beginning o the 80's, starting with the work by L. Shepp and Y. Vardi, the maximum likelihood approach using the expectation maximization (EM) algorithm has been a powerful tool to solve the estimation problem arising in emission computed tomography (ECT). Important drawbacks of this approach were: slowness of the EM algorithm and its inherent difficult to extend it to handle 'a priori' information. Recently, we presented a new EM-like algorithm, that is based on a decomposition by blocks, with one or more projections in each block, achieving a sped-up of tow orders of magnitudes. On the other hand, in 1995, we extended the EM algorithm, in a natural way, allowing regularization terms. In this article, we present the extension of our work to the case of regularized likelihood estimation; that is, a method that preserves the main properties of the one, but significantly faster, allowing fast Bayesian estimation in ECT. We illustrate the practical behavior of our method with PET simulations.

Citation Download Citation

Alvaro R. De Pierro "Fast Bayesian estimation methods in emission tomography", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323789

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