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

## TL;DR

This paper introduces 3DAngioNet, a deep learning system that rapidly reconstructs 3D coronary vessels from two-view X-ray images, improving accuracy and efficiency over existing methods.

## Contribution

The study presents a novel deep learning approach combining mesh segmentation and graph convolutional networks for automated 3D coronary vessel reconstruction from bi-plane angiography.

## Key findings

- Outperforms existing automated reconstruction methods
- Enables fast and accurate 3D vessel modeling
- Supports bifurcated vessel reconstruction

## Abstract

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.