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

Volume: 76, Pages: 294 - 306
Published: Aug 1, 2020
Abstract
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...
Paper Details
Title
Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.
Published Date
Aug 1, 2020
Volume
76
Pages
294 - 306
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