Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/195003
Type: Artigo de periódico
Title: Fast Em-like Methods For Maximum A Posteriori" Estimates In Emission Tomography."
Author: de Pierro, A R
Beleza Yamagishi, M E
Abstract: The maximum-likelihood (ML) approach in emission tomography provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. The expectation-maximization (EM) algorithm is an iterative algorithm for maximizing the Poisson likelihood in emission computed tomography that became very popular for solving the ML problem because of its attractive theoretical and practical properties. Recently, (Browne and DePierro, 1996 and Hudson and Larkin, 1994) block sequential versions of the EM algorithm that take advantage of the scanner's geometry have been proposed in order to accelerate its convergence. In Hudson and Larkin, 1994, the ordered subsets EM (OS-EM) method was applied to the ML problem and a modification (OS-GP) to the maximum a posteriori (MAP) regularized approach without showing convergence. In Browne and DePierro, 1996, we presented a relaxed version of OS-EM (RAMLA) that converges to an ML solution. In this paper, we present an extension of RAMLA for MAP reconstruction. We show that, if the sequence generated by this method converges, then it must converge to the true MAP solution. Experimental evidence of this convergence is also shown. To illustrate this behavior we apply the algorithm to positron emission tomography simulated data comparing its performance to OS-GP.
Subject: Algorithms
Image Processing, Computer-assisted
Likelihood Functions
Mathematical Computing
Phantoms, Imaging
Poisson Distribution
Tomography, Emission-computed
Rights: fechado
Identifier DOI: 10.1109/42.921477
Address: http://www.ncbi.nlm.nih.gov/pubmed/11370895
Date Issue: 2001
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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