PET imaging of atherosclerosis: artificial intelligence applications and recent advancements.

Abstract

PET imaging has become a valuable tool for assessing atherosclerosis by targeting key processes such as inflammation and microcalcification. Among available tracers, 18F-sodium fluoride has demonstrated superior performance compared to 18F-fluorodeoxyglucose, particularly in detecting coronary artery disease. However, the role of other tracers remains underexplored, requiring further validation. Emerging technologies such as artificial intelligence show potential in enhancing diagnostic speed and accuracy. Furthermore, the integration of the Alavi-Carlsen Calcification Score offers a novel approach to evaluating global disease burden, presenting a more clinically applicable method for predicting outcomes. Techniques such as total-body PET provide faster and more comprehensive imaging of the entire vascular system with reduced radiation exposure, representing a significant advancement in early detection and intervention. The combination of molecular imaging and advanced computational tools may revolutionize the management of atherosclerosis, facilitating earlier identification of at-risk individuals and improving long-term cardiovascular outcomes.