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Titolo:
Likelihood maximization for list-mode emission tomographic image reconstruction
Autore:
Byrne, C;
Indirizzi:
Univ Massachusetts, Dept Math Sci, Lowell, MA 01854 USA Univ Massachusetts Lowell MA USA 01854 ept Math Sci, Lowell, MA 01854 USA
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 10, volume: 20, anno: 2001,
pagine: 1084 - 1092
SICI:
0278-0062(200110)20:10<1084:LMFLET>2.0.ZU;2-T
Fonte:
ISI
Lingua:
ENG
Soggetto:
EM ALGORITHM; MAXIMUM-LIKELIHOOD; TRANSMISSION TOMOGRAPHY;
Keywords:
emission tomography; image reconstruction; iterative algorithm; probabilistic mixture;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
31
Recensione:
Indirizzi per estratti:
Indirizzo: Byrne, C Univ Massachusetts, Dept Math Sci, Lowell, MA 01854 USA Univ Massachusetts Lowell MA USA 01854 Sci, Lowell, MA 01854 USA
Citazione:
C. Byrne, "Likelihood maximization for list-mode emission tomographic image reconstruction", IEEE MED IM, 20(10), 2001, pp. 1084-1092

Abstract

The maximum a posteriori (MAP) Bayesian iterative algorithm using priors that are gamma distributed, due to Lange, Balm and Little, is extended to include parameter choices that fall outside the gamma distribution model. Special cases of the resulting iterative method include the expectation maximization maximum likelihood (EMML) method based on the Poisson model in emission tomography, as well as algorithms obtained by Parra and Barrett and by Huesman et al. that converge to maximum likelihood and maximum conditional likelihood estimates of radionuclide intensities for list-mode emission tomography. The approach taken here is optimization-theoretic and does not rely on the usual expectation maximization (EM) formalism. Block-iterative variants of the algorithms are presented. A self-contained, elementary proof of convergence of the algorithm is included.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 20/01/20 alle ore 22:01:03