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Proceedings Paper

Fast Bayesian estimation methods in emission tomography
Author(s): Alvaro R. De Pierro
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Paper 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.

Paper Details

Date Published: 22 September 1998
PDF: 6 pages
Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323789
Show Author Affiliations
Alvaro R. De Pierro, State Univ. of Campinas (United States)

Published in SPIE Proceedings Vol. 3459:
Bayesian Inference for Inverse Problems
Ali Mohammad-Djafari, Editor(s)

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