A Multi-Stage Deep Learning Pipeline for 3D Coronary Artery Reconstruction from X-Ray Angiograms

Coronary artery disease (CAD) is a leading cause of death worldwide. X-ray coronary angiograms are a diagnostic imaging technique, considered as the benchmark for identifying vascular anomalies. The two-dimensional nature of coronary angiograms limits spatial understanding that can complicate the diagnosis and cause misinformation. This paper presents a multi-stage pipeline leveraging deep learning techniques for reconstructing three-dimensional coronary vasculature from twodimensional X-ray angiograms. The pipeline involves various stages such as pre-processing, keyframe selection, image registration, segmentation and three-dimensional reconstruction, using image processing and deep learning techniques. Additionally, a synthetically generated coronary tree dataset is used to train the neural network for reconstruction and ensure accurate centerline predictions. The method is successful in generating precise reconstructions, overcoming the spatial limitations of twodimensional imaging.