AngioFM: a Vision Foundation Model for X-ray Coronary Angiography

X-ray coronary angiography (CAG) presents complex scenes in which coronary vessels, surrounding anatomy, and interventional devices overlap in two-dimensional projections. Cardiac motion, contrast filling, and changing projection geometry further complicate representation learning. We introduce AngioFM, a vision foundation model that uses these structured variations as supervision through feature prediction conditioned on acquisition time, contrast state, and imaging rays. Pretraining combines 31,559 unlabeled coronary videos with projections from 1,000 coronary CT angiography (CCTA) cases, coupling real angiographic dynamics with explicit cross-view, vessel-tangent, and branch supervision. We assess frozen representations using linear probes for controlled comparisons of accessible pathological, anatomical, and temporal information, complemented by CNN decoders for anatomical and device segmentation and a direct centerline-regression readout. With matched readouts and frozen encoders, AngioFM improves coronary-segment parsing over generic image-pretrained and CAG-pretrained baselines, with additional gains in video-based catheter and guidewire segmentation. Linear probes show higher coronary-dominance AUC, more accurate cardiac-phase matching, and competitive lesion localization. Centerline regression shows improved vessel localization in single-view and multi-view settings. Branch-supervision ablations further support improved anatomical discrimination. Together, these findings support learning reusable coronary representations from acquisition physics and vascular structure, providing a foundation for tools supporting angiographic assessment and image-guided intervention.