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Proceedings Paper

A Bayesian Reconstruction Algorithm for Emission Tomography using a Markov Random Field Prior
Author(s): Tom Hebert; Richard Leahy
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Paper Abstract

A Bayesian generalized expectation - maximization (GEM) algorithm using a locally correlated Markov random field prior in the form of a Gibbs function is developed for emission tomography. A close-form coordinate gradient ascent M-step which updates the image pixels sequentially is derived. The resulting GEM Bayesian algorithm is applied to estimating the 3-D image parameters in the Poisson model of emission sources based upon simulation of a parallel collimated gamma camera.

Paper Details

Date Published: 25 May 1989
PDF: 9 pages
Proc. SPIE 1092, Medical Imaging III: Image Processing, (25 May 1989); doi: 10.1117/12.953287
Show Author Affiliations
Tom Hebert, University of Southern California (United States)
Richard Leahy, University of Southern California (United States)

Published in SPIE Proceedings Vol. 1092:
Medical Imaging III: Image Processing
Samuel J. Dwyer III; R. Gilbert Jost M.D.; Roger H. Schneider, Editor(s)

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