[
    {
        "id": "osp-17849",
        "type": "article-journal",
        "title": "PPCAR-Net: Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views",
        "author": [
            {
                "family": "Ren",
                "given": "Yu"
            },
            {
                "family": "Lee",
                "given": "Hwee Kuan"
            },
            {
                "family": "Cham",
                "given": "Tat-Jen"
            },
            {
                "family": "Yap",
                "given": "Jonathan"
            },
            {
                "family": "Yeo",
                "given": "Khung Keong"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/ppcar-net-projection-refined-parametric-3d-coronary-artery-reconstruction-from-sparse-x-ray-angiographic-views",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Sparse-view 3D coronary reconstruction commonly relies on cross-view correspondence and triangulation, which are vulnerable to vessel overlap and foreshortening, or on volumetric prediction followed by vascular-graph extraction, which does not directly provide centrelines and radii. We introduce PPCAR-Net, a projection-refined parametric coronary artery reconstruction network that directly predicts a branch-structured centreline-and-radius representation without explicit point matching, triangulation, or an intermediate volume. Given a variable number of segmented views, a coarse predictor combines frozen VGGT features with learned branch queries to estimate branch presence, B-spline centreline trajectories, and dense radius profiles. Projection-guided geometry and radius refiners then sample local evidence from the input views and apply residual corrections learned with 3D supervision. We evaluate representation fidelity and sparse-view reconstruction quantitatively and qualitatively. On simulated angiographic masks generated from CT-derived coronary anatomy, PPCAR-Net produces better connected artery reconstructions and achieves strong centreline accuracy, particularly for RCA, while maintaining competitive volumetric overlap. Coarse-to-fine inference takes 121 ms, enabling real-time reconstruction."
    }
]