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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Bibliographic Details

Published in:	2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Vol. 2015; pp. 1 - 3
Main Author:	Zeng, Gengsheng L.
Format:	Conference Proceeding Journal Article
Language:	English
Published:	United States IEEE 01.10.2015
Subjects:	Algorithm design and analysis Approximation algorithms Image reconstruction Noise measurement Phantoms Photonics noise weighted image reconstruction maximum likelihood (ML) Computed tomography positron emission tomography (PET) single photon emission computed tomography (SPECT) iterative reconstruction expectation maximization (EM) Poisson noise
ISSN:	0018-9499
Online Access:	Get full text
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