Anatomy-Grounded Synthetic Coronary Angiography for Geometry-Informed Multi-View Matching
For researchers in interventional cardiology and medical image analysis, this work provides a scalable solution to the lack of labeled training data for correspondence matching in coronary angiography.
The authors tackle the data bottleneck in multi-view coronary angiography matching by introducing a synthetic data generation framework that creates high-fidelity Digital Reconstructed Radiographs (DRRs) with dense ground-truth labels from 3D CCTA volumes. Their Geometry-Informed Matching Module (GIMM) outperforms baseline matching models on this dataset.
Accurate correspondence matching across multiple angiographic views is the prerequisite for 3D coronary reconstruction and interventional guidance. However, the development of robust deep learning models for this task has been stifled by a fundamental data bottleneck. Obtaining ground truth for matching tasks in angiography pairs is prohibitively expensive and hard to scale. To overcome this barrier, we introduce a physically-grounded data generation framework that synthesizes high-fidelity Digital Reconstructed Radiographs (DRRs) from 3D Coronary CT Angiography (CCTA) volumes. Our framework generates dense, highly accurate 3D-to-2D projection labels by simulating realistic C-arm acquisition geometry on patient anatomy at zero human cost. Leveraging this dense supervision, we propose a Geometry-Informed Matching Module (GIMM) that integrates global feature and anatomical structure into correspondence learning. Unlike real angiography where assessment relies on subjective human annotation, our dataset provides 2D correspondence labels with paired images, allowing human-free evaluation. We comprehensively evaluate our method on the proposed CT-derived DRR dataset and demonstrate improvements over other matching baseline models.