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X-ray coronary angiography (XCA) is used to assess coronary artery diseaseand provides valuable information on lesion morphology and severity. However,XCA images are 2D and therefore limit visualisation of the vessel. 3Dreconstruction of coronary vessels is possible using multiple views, howeverlumen border detection in current software is performed manually resulting inlimited reproducibility and slow processing time. In this study we propose3DAngioNet, a novel deep learning (DL) system that enables rapid 3D vessel meshreconstruction using 2D XCA images from two views. Our approach learns a coarsemesh template using an EfficientB3-UNet segmentation network and projectiongeometries, and deforms it using a graph convolutional network. 3DAngioNetoutperforms similar automated reconstruction methods, offers improvedefficiency, and enables modelling of bifurcated vessels. The approach wasvalidated using state-of-the-art software verified by skilled cardiologists.

3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks