Classification of Myocardial 18 F-FDG PET Uptake Patterns Using Deep Learning

F 18 ( 18F) fluorodeoxyglucose (FDG) PET is used to evaluate cardiac tumors, inflammation, and myocardial viability. As a glucose analog, FDG is sensitive to carbohydrate metabolism, and increased FDG uptake implies a shift from lipid to carbohydrate metabolism. During fasting, the myocardium predominantly consumes free fatty acids and triglycerides with minimal carbohydrate metabolism (1). This metabolic pattern is reversed following a meal. Heart disease shows similar alterations in the metabolic shift from lipid to glucose metabolism (2), which has been attributed to the susceptibility of free fatty acid b-oxidation to ischemia (3–5). Nonischemic heart disease also shows increased carbohydrate usage (6,7). The shifting metabolism in normal myocardium and in patients with cardiac disease can result in different spatiotemporal uptake patterns at FDG PET (8). Investigators have spatially classified these patterns using several classification schemes from oncologic studies (9,10). The classification is qualitative, and there is variability among experts because of uncertainty and overlap in uptake patterns. There is a need for robust and automatic techniques to classify patterns of myocardial FDG PET uptake to improve consistency of diagnosis between patients with similar uptake patterns and among caregivers reviewing the same images. Likewise, fully automated segmentation and classification methods of left ventricle (LV) uptake patterns are needed to develop large-scale investigations of their clinical significance and to distinguish between physiologic and pathologic LV uptake. Such large-scale repositories are increasingly becoming available in health biobanks (11). There is a need for automated techniques to extract quantitative imaging traits such as myocardial uptake and pattern of classification from this imaging data at scale. To address the challenges of conventional analysis of large numbers of images and to improve the consistency of diagnosis, machine learning can provide precise image labeling using automation (12,13). Recent deep learning techniques have reached a high level of automated performance approaching or, in some cases, exceeding experts (14). An area to be explored is applying classification and