CoroLift: Fast Single-View 3D Coronary Reconstruction with Anatomical Priors

3D coronary reconstruction from 2D observations is important for clinical hemodynamic assessment, yet is inherently ill-posed, particularly in the single-view setting without multi-view geometric cues. Existing methods therefore typically rely on multiple views, leaving single-view reconstruction largely unexplored. Our key idea is to leverage anatomical priors to constrain the missing 3D geometry from a single view. We propose **CoroLift**, a coarse-to-fine framework for single-view 3D coronary reconstruction that integrates anatomical priors on topology, position, and morphology into initialization and refinement. To construct these priors, we decompose coronary trees into slots between bifurcations and endpoints, with shared anatomical identities across patients. We derive a topology prior from population-level connectivity statistics, a position prior from spatial distributions, and a morphology prior that combines family-specific statistical shape models with tree-structured inter-slot dependencies. CoroLift first formulates reconstruction as depth estimation along imaging rays. Topology and position priors guide residual-based 3D endpoint localization and continuous branch-depth estimation, yielding an initial 3D coronary tree. The morphology prior then regularizes the joint vessel geometry while maintaining consistency with the 2D observations. Experiments show that CoroLift reduces geometric reconstruction error by **26.6%** compared with existing baselines, while substantially improving morphological fidelity and reconstructing each 3D coronary tree in only **0.17 s**.