Machine learning in quantitative PET : A review of attenuation correction and low-count image reconstruction methods
Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved..
The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning methodology, ranging from random forest and dictionary learning to the latest convolutional neural network-based architectures. For application in PET attenuation correction, two general strategies are reviewed: 1) generating synthetic CT from MR or non-AC PET for the purposes of PET AC, and 2) direct conversion from non-AC PET to AC PET. For low-count PET reconstruction, recent deep learning-based studies and the potential advantages over conventional machine learning-based methods are presented and discussed. In each application, the proposed methods, study designs and performance of published studies are listed and compared with a brief discussion. Finally, the overall contributions and remaining challenges are summarized.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:76 |
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| Enthalten in: |
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) - 76(2020) vom: 01. Aug., Seite 294-306 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Tonghe [VerfasserIn] |
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| Links: |
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| Themen: |
Attenuation correction |
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| Anmerkungen: |
Date Completed 24.06.2021 Date Revised 29.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ejmp.2020.07.028 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM313142475 |
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| 520 | |a Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved. | ||
| 520 | |a The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning methodology, ranging from random forest and dictionary learning to the latest convolutional neural network-based architectures. For application in PET attenuation correction, two general strategies are reviewed: 1) generating synthetic CT from MR or non-AC PET for the purposes of PET AC, and 2) direct conversion from non-AC PET to AC PET. For low-count PET reconstruction, recent deep learning-based studies and the potential advantages over conventional machine learning-based methods are presented and discussed. In each application, the proposed methods, study designs and performance of published studies are listed and compared with a brief discussion. Finally, the overall contributions and remaining challenges are summarized | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a Attenuation correction | |
| 650 | 4 | |a Low-count PET | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a PET | |
| 650 | 4 | |a Positron emission tomography | |
| 700 | 1 | |a Lei, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Yabo |e verfasserin |4 aut | |
| 700 | 1 | |a Curran, Walter J |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tian |e verfasserin |4 aut | |
| 700 | 1 | |a Nye, Jonathon A |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaofeng |e verfasserin |4 aut | |
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