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Titolo:
Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations
Autore:
Nuyts, J; Michel, C; Dupont, P;
Indirizzi:
Katholieke Univ Leuven, Dept Nucl Med, B-3000 Louvain, Belgium Katholieke Univ Leuven Louvain Belgium B-3000 d, B-3000 Louvain, Belgium Univ Catholique Louvain, PET Lab, B-1348 Louvain, Belgium Univ Catholique Louvain Louvain Belgium B-1348 , B-1348 Louvain, Belgium
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 5, volume: 20, anno: 2001,
pagine: 365 - 375
SICI:
0278-0062(200105)20:5<365:MEROSW>2.0.ZU;2-Y
Fonte:
ISI
Lingua:
ENG
Soggetto:
POSITRON-EMISSION-TOMOGRAPHY; IMAGE-RECONSTRUCTION; ITERATIVE RECONSTRUCTION; ATTENUATION CORRECTION; ALGORITHMS; SCANNER;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
18
Recensione:
Indirizzi per estratti:
Indirizzo: Nuyts, J Katholieke Univ Leuven, Dept Nucl Med, B-3000 Louvain, Belgium Katholieke Univ Leuven Louvain Belgium B-3000 Louvain, Belgium
Citazione:
J. Nuyts et al., "Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations", IEEE MED IM, 20(5), 2001, pp. 365-375

Abstract

The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for image reconstruction in positron emission tomography, The algorithm is strictly valid if the data are Poisson distributed. However, it is also often applied to processed sinograms that do not meet this requirement, This may sometimes lead to suboptimal results: streak artifacts appear and the algorithm converges toward a lower likelihood value. As a remedy, we propose two simple pixel-by-pixel methods [noise equivalent counts (NEC)-scaling and NEC-shifting] in order to transform arbitrary sinogram noise into noise which is approximately Poisson distributed (the firstand second moments of the distribution match those of the Poisson distribution). The convergence speed associated with both transformation methods iscompared, and the NEC-scaling method is validated with both simulations and clinical data,These new methods extend the ML-EM algorithm to a general purpose nonnegative reconstruction algorithm.

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Documento generato il 20/01/20 alle ore 07:38:02