用3D冠脉影像生成逼真二维血管图像，解决多视角匹配数据难的问题。
Anatomy-Grounded Synthetic Coronary Angiography for Geometry-Informed Multi-View Matching
- 基于3D冠脉CT模拟真实设备成像，自动生成高保真2D投影图
- 提出几何感知匹配模块，融合全局特征与解剖结构提升匹配精度
- 无需人工标注，可实现自动化评估，适合医学影像三维重建研究者
准确的多视角血管影像对应匹配是实现冠脉三维重建与介入导航的前提。然而，由于真实配准标签获取成本极高且难以扩展，深度学习模型的发展受到严重制约。为此，本文提出一种物理基础的数据生成框架，从3D冠脉计算机断层扫描血管造影（CCTA）体积中合成高保真数字重建放射图像（DRRs）。该框架通过模拟真实的C臂成像几何，在零人力成本下生成密集、精确的3D到2D投影标签。利用这些密集监督信号，我们提出几何感知匹配模块（GIMM），将全局特征与解剖结构融入对应学习。与依赖主观人工标注的真实血管影像不同，本数据集提供成对图像及2D对应标签，支持无须人工参与的评估。我们在所提出的基于CT的DRR数据集上全面评估方法，并显著优于其他匹配基线模型。
原文摘要 · Abstract (English)
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.
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