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:2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) Jg. 2015; S. 1 - 3
1. Verfasser: Zeng, Gengsheng L.
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2015
Schlagworte:
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
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Zusammenfassung: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 approximate solution that is close to the true solution. It is well-known that the ML-EM algorithm at early iterations converges towards the true solution and then in later iterations diverges away from the true solution. Therefore a potential good approximate solution can only be obtained by early termination. This short paper argues that the ML-EM algorithm is not optimal in providing such an approximate solution. In order to show that the ML-EM algorithm is not optimal, it is only necessary to provide a different algorithm that performs better. An alternative algorithm is suggested in this paper and this alternative algorithm is able to outperform the ML-EM algorithm.
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ISSN:0018-9499
DOI:10.1109/NSSMIC.2015.7582178