Machine Learning in Quantitative PET Imaging
This is an incremental review paper that synthesizes existing research for researchers in medical imaging and PET analysis.
This paper reviewed machine learning methods for quantitative PET imaging, specifically summarizing developments in PET attenuation correction and low-count PET reconstruction by comparing proposed methods, study designs, and reported performances from existing studies.
This paper reviewed the machine learning-based studies for quantitative positron emission tomography (PET). Specifically, we summarized the recent developments of machine learning-based methods in PET attenuation correction and low-count PET reconstruction by listing and comparing the proposed methods, study designs and reported performances of the current published studies with brief discussion on representative studies. The contributions and challenges among the reviewed studies were summarized and highlighted in the discussion part followed by.