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 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.