3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks
This work addresses the need for faster and more reproducible 3D vessel reconstruction in coronary artery disease assessment, though it appears incremental as it builds on existing automated methods.
The study tackled the problem of slow and manual 3D reconstruction of coronary vessels from 2D X-ray angiography by proposing 3DAngioNet, a deep learning system that automates the process and outperforms similar automated methods in efficiency and ability to model bifurcated vessels.
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.