The ML-EM Algorithm is Not Optimal for Poisson Noise

The ML-EM (maximum likelihood expectation maximization) algorithm is the most popular image reconstruction method when the measurement noise is Poisson distributed. This short paper considers the problem that for a given noisy projection data set, whether the ML-EM algorithm is able to provide an ap...

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Veröffentlicht in:	IEEE transactions on nuclear science Jg. 62; H. 5; S. 2096 - 2101
1. Verfasser:	Zeng, Gengsheng L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation algorithms Bayesian analysis Computed tomography Estimating techniques expectation maximization (EM) Image reconstruction iterative reconstruction maximum likelihood (ML) Noise Noise level Noise measurement noise weighted image reconstruction Phantoms Poisson noise Positron emission tomography positron emission tomography (PET) single photon emission computed tomography (SPECT)
ISSN:	0018-9499, 1558-1578
Online-Zugang:	Volltext
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