Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans. At its core is CARD-ViT, a self-supervised Vision Transformer trained exclusively on gated CT data using DINO. Without any non-gated training data, our framework achieves 0.707 accuracy and a Cohen's kappa of 0.528 on the Stanford non-gated dataset, matching models trained directly on non-gated scans. On gated test sets, the framework achieves 0.910 accuracy with Cohen's kappa scores of 0.871 and 0.874 across independent datasets, demonstrating robust risk stratification. These results demonstrate the feasibility of cross-domain CAC scoring from gated to non-gated domains, supporting scalable cardiovascular screening in routine chest imaging without additional scans or annotations.

翻译：冠状动脉钙化（CAC）评分是心血管风险的关键预测指标，但其依赖于心电图门控CT扫描，限制了其在专业心脏成像场景中的应用。我们提出了一种自动化框架，用于CAC检测及病灶特异性Agatston评分，该框架可在门控与非门控CT扫描中通用。其核心是CARD-ViT，一种基于DINO方法、仅使用门控CT数据训练的自监督Vision Transformer。在未使用任何非门控训练数据的情况下，我们的框架在斯坦福非门控数据集上达到了0.707的准确率和0.528的Cohen's kappa系数，与直接使用非门控扫描训练的模型性能相当。在门控测试集上，该框架在独立数据集中分别实现了0.910的准确率以及0.871和0.874的Cohen's kappa系数，展现出稳健的风险分层能力。这些结果证明了从门控域到非门控域进行跨域CAC评分的可行性，为在常规胸部成像中开展可扩展的心血管筛查提供了支持，且无需额外扫描或标注。