Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States.
Front Public Health. 2022 Aug 12;10:915615. doi: 10.3389/fpubh.2022.915615. eCollection 2022.
To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.
A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again.
The median (interquartile range) lesion volume (cm) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, < 0.05, presumably due to the consideration of all the infected areas at baseline.
The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.
评估连续 CT 成像监测疾病演变和治疗反应的 COVID-19 病变的体积变化。
共确定了 48 名经胸部 CT 检查证实患有 COVID-19 感染的患者，其中男性 28 名，女性 20 名。年龄范围为 21-93 岁，平均年龄为 54±18 岁。其中，33 名患者接受了第一次随访（F/U）扫描，29 名患者接受了第二次 F/U 扫描，11 名患者接受了第三次 F/U 扫描。手动勾勒出病变感兴趣区（ROI）。开发了一种两步配准方法，首先使用仿射配准，然后使用非刚性 Demons 算法，将基线和 F/U 图像上的肺区域进行匹配。使用获得的几何变换矩阵将基线病变 ROI 映射到 F/U 图像上，然后由放射科医生在 F/U CT 上再次勾勒病变 ROI。
中位数（四分位距）病变体积（cm）基线 CT 检查为 30.9（83.1），第一次 F/U 为 18.3（43.9），第二次 F/U 为 7.6（18.9），第三次 F/U 为 0.6（19.1），随时间呈显著下降趋势。两步配准可以显著降低基线和 F/U 图像之间的均方误差（MSE）， < 0.001。该方法可以匹配肺区域和肺内的大血管。当使用映射的基线 ROI 作为参考时，第二次 ROI 绘制显示体积显著增加，<0.05，可能是由于在基线时考虑了所有感染区域。
结果表明，该配准方法可应用于辅助评估 COVID-19 病变在胸部 CT 上的纵向变化。
