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Proceedings Paper

Unified approach to regularized maximum-likelihood estimation in computed tomography
Author(s): Alvaro R. De Pierro
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Paper Abstract

Since 1982, when it was first proposed by Shepp and Vardi, the Expectation Maximization (EM) algorithm has become very popular among researchers in image reconstruction. Recently, a natural extension of the EM algorithm was proposed in order to handle regularization terms containing `a priori' information for emission computed tomography problems. This new idea was further applied to other regularized maximum likelihood problems in transmission and emission tomography. We present in this article a unified approach to more general regularized ML problems. Our convergence proofs also extend those given in the previous papers allowing more general regularizations. We report on numerical simulations.

Paper Details

Date Published: 9 December 1997
PDF: 7 pages
Proc. SPIE 3171, Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications, (9 December 1997); doi: 10.1117/12.279727
Show Author Affiliations
Alvaro R. De Pierro, State Univ. of Campinas (United States)


Published in SPIE Proceedings Vol. 3171:
Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)

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