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. 1989;8(2):194-202.
doi: 10.1109/42.24868.

A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors

Affiliations

A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors

T Hebert et al. IEEE Trans Med Imaging. 1989.

Abstract

A generalized expectation-maximization (GEM) algorithm is developed for Bayesian reconstruction, based on locally correlated Markov random-field priors in the form of Gibbs functions and on the Poisson data model. For the M-step of the algorithm, a form of coordinate gradient ascent is derived. The algorithm reduces to the EM maximum-likelihood algorithm as the Markov random-field prior tends towards a uniform distribution. Three different Gibbs function priors are examined. Reconstructions of 3-D images obtained from the Poisson model of single-photon-emission computed tomography are presented.

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