Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.

A novel copper-mediated carboxylation strategy of aryl- and heteroaryl-stannanes is described. The method serves as a mild (i.e., 1 atm) carboxylation method using stable carbon dioxide and is transferable as a radiosynthetic approach for carbon-11-labeled aromatic and heteroaromatic carboxylic acids using sub-stoichiometric quantities of [11C]­CO2. The methodology was applied to the radiosynthesis of the retinoid X receptor agonist, [11C]­bexarotene, with a decay-corrected radiochemical yield of 32 ± 5% and molar activity of 38 ± 23 GBq/μmol (n = 3).