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 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2015 |
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| Erschienen: |
2015 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2015 |
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| Enthalten in: |
IEEE transactions on nuclear science - 2015(2015) vom: 21. Okt. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zeng, Gengsheng L [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 27.03.2024 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/NSSMIC.2015.7582178 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM276063597 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Poisson noise | |
| 650 | 4 | |a expectation maximization (EM) | |
| 650 | 4 | |a iterative reconstruction | |
| 650 | 4 | |a maximum likelihood (ML) | |
| 650 | 4 | |a noise weighted image reconstruction | |
| 650 | 4 | |a positron emission tomography (PET) | |
| 650 | 4 | |a single photon emission computed tomography (SPECT) | |
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