Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views

Sparse-view 3D coronary reconstruction is commonly formulated as cross-view correspondence and triangulation, which is vulnerable to vessel overlap and foreshortening, or on volumetric prediction followed by vascular-graph extraction, which does not natively provide centrelines and radii. We address both limitations with a coarse-to-fine framework that avoids explicit point matching and triangulation while directly predicting a branch-structured centreline-and-radius representation instead of an intermediate volume. From a variable number of segmented views, a coarse predictor combines frozen VGGT features with learned branch queries to estimate active branches, their B-spline centreline trajectories, and approximate dense radius profiles. Projection-guided geometry and radius refiners sample input view evidence and predict residual corrections learned with 3D supervision. We evaluate representation fidelity and sparse-view 3D reconstruction quantitatively and qualitatively. On simulated angiographic masks generated from CT-derived coronary anatomy, our method achieves the best structural and centreline metrics among the evaluated methods while maintaining competitive volumetric overlap. Coarse-to-fine inference takes $121$ ms, enabling real-time reconstruction.