X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity. However, XCA images are 2D and therefore limit visualisation of the vessel. 3D reconstruction of coronary vessels is possible using multiple views, however lumen border detection in current software is performed manually resulting in limited reproducibility and slow processing time. In this study we propose 3DAngioNet, a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach learns a coarse mesh template using an EfficientB3-UNet segmentation network and projection geometries, and deforms it using a graph convolutional network. 3DAngioNet outperforms similar automated reconstruction methods, offers improved efficiency, and enables modelling of bifurcated vessels. The approach was validated using state-of-the-art software verified by skilled cardiologists.

翻译：X射线冠状动脉造影（XCA）用于评估冠状动脉疾病，并提供病变形态及严重程度的重要信息。然而，XCA图像为二维图像，因此限制了血管的可视化。利用多视角可实现冠状动脉血管的三维重建，但现有软件中管腔边界检测依赖人工操作，导致可重复性差且处理时间长。本研究提出3DAngioNet——一种新型深度学习系统，能够利用两个视角的二维XCA图像实现快速的三维血管网格重建。该方法通过EfficientB3-UNet分割网络与投影几何学学习粗网格模板，并利用图形卷积网络对其进行形变处理。3DAngioNet性能优于同类自动化重建方法，具有更高效率，并能实现分叉血管建模。该方法已通过经验丰富的心脏病专家验证的先进软件进行评估。