AI-powered SPOT imaging for enhanced myocardial scar detection and quantification

Abstract

Cardiovascular disease is the leading global cause of death, underscoring the need for accurate assessment of myocardial injury. The current gold standard, bright-blood late gadolinium enhanced MRI, suffers from poor contrast at the blood-scar interface, reducing sensitivity for subendocardial scar detection and limiting reproducibility. Moreover, reliance on expert manual analysis makes interpretation labor-intensive and variable. Here, we present SPOT, a multi-spectral bright- and black-blood imaging sequence that provides unprecedented scar-to-blood contrast and clear anatomical detail. Integrated with an artificial intelligence (AI) framework for automated image analysis, SPOT enables rapid, fully automated, and operator-independent quantification of myocardial injury. Validated in simulations, animal infarct models, and patients with heart disease, this combined imaging-AI platform delivers accurate detection and quantification in a single acquisition. This innovation presents significant opportunities for earlier diagnosis and enhanced therapeutic management of ischemic heart disease, with potential applications in a wide spectrum of other clinical settings.