title: A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors.
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.
authors.affiliation: Signal and Image Process. Inst., Univ. of Southern California, Los Angeles, CA.
authors.forename: T
authors.initials: T
authors.lastname: Hebert
authors.forename: R
authors.initials: R
authors.lastname: Leahy