Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respiratory motion. ECG-gated cardiac CTs substantially reduce motion artifacts, but their use in population screening and routine imaging remains limited due to gating requirements and lack of insurance coverage. Although identification of incidental CAC from non-gated chest CT is increasingly considered for it offers an accessible and widely available alternative, this modality is limited by more severe motion artifacts. We present ProDM (Property-aware Progressive Correction Diffusion Model), a generative diffusion framework that restores motion-free calcified lesions from non-gated CTs. ProDM introduces three key components: (1) a CAC motion simulation data engine that synthesizes realistic non-gated acquisitions with diverse motion trajectories directly from cardiac-gated CTs, enabling supervised training without paired data; (2) a property-aware learning strategy incorporating calcium-specific priors through a differentiable calcium consistency loss to preserve lesion integrity; and (3) a progressive correction scheme that reduces artifacts gradually across diffusion steps to enhance stability and calcium fidelity. Experiments on real patient datasets show that ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines. A reader study on real non-gated scans further confirms that ProDM suppresses motion artifacts and improves clinical usability. These findings highlight the potential of progressive, property-aware frameworks for reliable CAC quantification from routine chest CT imaging.

翻译：胸部CT冠状动脉钙化（CAC）评分是用于分层和优化临床心血管疾病风险评估的成熟工具。CAC量化依赖于钙化病灶的精确勾画，但常受心脏和呼吸运动引入的伪影影响。心电图门控心脏CT能显著减少运动伪影，但由于门控要求及缺乏保险覆盖，其在人群筛查和常规成像中的应用仍受限。尽管从非门控胸部CT中识别偶发CAC因其可及性与广泛可用性而日益受到关注，但该模态受更严重运动伪影的限制。本文提出ProDM（属性感知渐进校正扩散模型），一种生成式扩散框架，可从非门控CT中恢复无运动钙化病灶。ProDM引入三个关键组件：（1）CAC运动模拟数据引擎，可直接从心脏门控CT合成具有多样化运动轨迹的真实非门控采集数据，实现无需配对数据的监督训练；（2）属性感知学习策略，通过可微钙化一致性损失融入钙化特异性先验以保持病灶完整性；（3）渐进校正方案，在扩散步骤中逐步减少伪影以增强稳定性和钙化保真度。在真实患者数据集上的实验表明，与多种基线方法相比，ProDM显著提升了CAC评分准确性、空间病灶保真度及风险分层性能。对真实非门控扫描的阅片研究进一步证实，ProDM能有效抑制运动伪影并提升临床可用性。这些发现凸显了渐进式属性感知框架在常规胸部CT成像中实现可靠CAC量化的潜力。