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Titolo:
Fast EM-like methods for maximum "a posteriori" estimates in emission tomography
Autore:
De Pierro, AR; Yamagishi, MEB;
Indirizzi:
Univ Estadual Campinas, Dept Appl Math, BR-13081970 Campinas, SP, Brazil Univ Estadual Campinas Campinas SP Brazil BR-13081970 BCpinas, SP, Brazil
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 4, volume: 20, anno: 2001,
pagine: 280 - 288
SICI:
0278-0062(200104)20:4<280:FEMFM">2.0.ZU;2-V
Fonte:
ISI
Lingua:
ENG
Soggetto:
EXPECTATION-MAXIMIZATION ALGORITHM; ITERATIVE IMAGE-RECONSTRUCTION; LIKELIHOOD-ESTIMATION; BAYESIAN RECONSTRUCTION; PET IMAGES; SYSTEM;
Keywords:
expectation-maximization algorithm; ordered subsets maximum-likelihood algorithm; positron emission tomography; regularization;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
42
Recensione:
Indirizzi per estratti:
Indirizzo: De Pierro, AR Univ Estadual Campinas, Dept Appl Math, CP 6065, BR-13081970Campinas, SP,Brazil Univ Estadual Campinas CP 6065 Campinas SP Brazil BR-13081970 BC
Citazione:
A.R. De Pierro e M.E.B. Yamagishi, "Fast EM-like methods for maximum "a posteriori" estimates in emission tomography", IEEE MED IM, 20(4), 2001, pp. 280-288

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 showthat, 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.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 19/01/20 alle ore 20:04:17