Coronary artery calcification (CAC) is a strong predictor of cardiovascular events, with CT-based Agatston scoring widely regarded as the clinical gold standard. However, CT is costly and impractical for large-scale screening, while chest X-rays (CXRs) are inexpensive but lack reliable ground truth labels, constraining deep learning development. Digitally reconstructed radiographs (DRRs) offer a scalable alternative by projecting CT volumes into CXR-like images while inheriting precise labels. In this work, we provide the first systematic evaluation of DRRs as a surrogate training domain for CAC detection. Using 667 CT scans from the COCA dataset, we generate synthetic DRRs and assess model capacity, super-resolution fidelity enhancement, preprocessing, and training strategies. Lightweight CNNs trained from scratch outperform large pretrained networks; pairing super-resolution with contrast enhancement yields significant gains; and curriculum learning stabilises training under weak supervision. Our best configuration achieves a mean AUC of 0.754, comparable to or exceeding prior CXR-based studies. These results establish DRRs as a scalable, label-rich foundation for CAC detection, while laying the foundation for future transfer learning and domain adaptation to real CXRs.

翻译：冠状动脉钙化（CAC）是心血管事件的重要预测因子，其中基于CT的Agatston评分被广泛视为临床金标准。然而，CT检查成本高昂且不适用于大规模筛查，而胸部X射线（CXR）虽成本低廉，却缺乏可靠的基准真值标签，这限制了深度学习技术的发展。数字重建放射影像（DRR）通过将CT体数据投影为类CXR图像，同时继承精确的标注信息，提供了一种可扩展的替代方案。本研究首次系统评估了DRR作为CAC检测替代训练域的有效性。利用COCA数据集的667例CT扫描，我们生成合成DRR图像，并评估模型容量、超分辨率保真度增强、预处理及训练策略。实验表明：从头训练的轻量级CNN优于大型预训练网络；超分辨率与对比度增强结合可带来显著性能提升；课程学习能在弱监督下稳定训练过程。我们的最优配置实现了0.754的平均AUC值，达到或超越了先前基于CXR的研究水平。这些结果确立了DRR作为可扩展、标注丰富的CAC检测基础，同时为未来向真实CXR的迁移学习与域适应研究奠定了基础。