Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods

•Machine learning has been integrated to PET in attenuation correction (AC) and low-count reconstruction in recent years.•The proposed methods, study designs and key results of the current published studies are reviewed in this paper.•Machine learning generates synthetic CT from MR or non-AC PET for...

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Bibliographic Details
Published in:Physica medica 2020-08, Vol.76, p.294-306
Main Authors: Wang, Tonghe, Lei, Yang, Fu, Yabo, Curran, Walter J., Liu, Tian, Nye, Jonathon A., Yang, Xiaofeng
Format: Article
Language:English
Subjects:
Attenuation correction
Brain
Image Processing, Computer-Assisted
Low-count PET
Machine Learning
Magnetic Resonance Imaging
Multimodal Imaging
PET
Positron emission tomography
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Summary:•Machine learning has been integrated to PET in attenuation correction (AC) and low-count reconstruction in recent years.•The proposed methods, study designs and key results of the current published studies are reviewed in this paper.•Machine learning generates synthetic CT from MR or non-AC PET for PET AC, or directly maps non-AC PET to AC PET.•Deep learning-based methods have advantages over conventional machine learning methods in low-count PET reconstruction. 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.
ISSN:1120-1797
1724-191X
DOI:10.1016/j.ejmp.2020.07.028