Longitudinal low-dose CT follow-ups vary in noise, reconstruction kernels, and registration quality. These differences destabilize subtraction images and can trigger false new lesion alarms. We present TopoGate, a lightweight model that combines the follow-up appearance view with the subtraction view and controls their influence through a learned, quality-aware gate. The gate is driven by three case-specific signals: CT appearance quality, registration consistency, and stability of anatomical topology measured with topological metrics. On the NLST--New-Lesion--LongCT cohort comprising 152 pairs from 122 patients, TopoGate improves discrimination and calibration over single-view baselines, achieving an area under the ROC curve of 0.65 with a standard deviation of 0.05 and a Brier score of 0.14. Removing corrupted or low-quality pairs, identified by the quality scores, further increases the area under the ROC curve from 0.62 to 0.68 and reduces the Brier score from 0.14 to 0.12. The gate responds predictably to degradation, placing more weight on appearance when noise grows, which mirrors radiologist practice. The approach is simple, interpretable, and practical for reliable longitudinal LDCT triage.

翻译：纵向低剂量CT随访图像在噪声、重建核和配准质量方面存在差异。这些差异会破坏减影图像的稳定性，并可能引发虚假的新病灶警报。我们提出了TopoGate，一种轻量级模型，它将随访外观视图与减影视图相结合，并通过一个学习的、质量感知的门控机制来控制两者的影响。该门控由三个针对具体病例的信号驱动：CT外观质量、配准一致性以及通过拓扑度量测量的解剖结构拓扑稳定性。在包含122名患者152对图像的NLST--New-Lesion--LongCT队列中，TopoGate在区分度和校准度上均优于单视图基线，其ROC曲线下面积为0.65（标准差0.05），Brier分数为0.14。通过质量评分识别并剔除损坏或低质量的图像对后，ROC曲线下面积从0.62提升至0.68，Brier分数从0.14降至0.12。该门控机制对图像质量下降的响应是可预测的，当噪声增大时，它会赋予外观视图更高的权重，这与放射科医师的实践相符。该方法简单、可解释且实用，可用于可靠的纵向低剂量CT分流。