AUTHOR=Chen Xiao , Zhang Yang , Cao Guoquan , Zhou Jiahuan , Lin Ya , Chen Boyang , Nie Ke , Fu Gangze , Su Min-Ying , Wang Meihao 

TITLE=Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.915615

DOI=10.3389/fpubh.2022.915615

ISSN=2296-2565

ABSTRACT=Purpose: To evaluate the volumetric change of COVID lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.   
Materials and Methods: 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 the mean of 5418. Of them, 33 received the first follow-up (F/U) scan, 29 received the second F/U, and 11 received the third F/U. 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. 
Results: The median (inter-quartile range) lesion volume (cm3) was 30.9 (83.1) at baseline; 18.3 (43.9) at first F/U; 7.6 (18.9) at second F/U; 0.6 (19.1) at third F/U, which showed a significant trend of decrease. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p<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, p<0.05, presumably due to the consideration all infected areas at baseline.
Conclusion: The results suggest that the registration method can be applied to assist in the evaluatin of longitudinal changes of COVID-19 lesions on chest CT.