Background and Objectives: To investigate whether coronavirus disease 2019 (COVID-19) survivors who had different disease severities have different levels of pulmonary sequelae at 3 months post-discharge.Methods: COVID-19 patients discharged from four hospitals 3 months previously, recovered asymptomatic patients from an isolation hotel, and uninfected healthy controls (HCs) from the community were prospectively recruited. Participants were recruited at Wuhan Union Hospital and underwent examinations, including quality-of-life evaluation (St. George Respiratory Questionnaire [SGRQ]), laboratory examination, chest computed tomography (CT) imaging, and pulmonary function tests.Results: A total of 216 participants were recruited, including 95 patients who had recovered from severe/critical COVID-19 (SPs), 51 who had recovered from mild/moderate disease (MPs), 28 who had recovered from asymptomatic disease (APs), and 42 HCs. In total, 154 out of 174 (88.5%) recovered COVID-19 patients tested positive for serum SARS-COV-2 IgG, but only 19 (10.9%) were still positive for IgM. The SGRQ scores were highest in the SPs, while APs had slightly higher SGRQ scores than those of HCs; 85.1% of SPs and 68.0% of MPs still had residual CT abnormalities, mainly ground-glass opacity (GGO) followed by strip-like fibrosis at 3 months after discharge, but the pneumonic lesions were largely absorbed in the recovered SPs or MPs relative to findings in the acute phase. Pulmonary function showed that the frequency of lung diffusion capacity for carbon monoxide abnormalities were comparable in SPs and MPs (47.1 vs. 41.7%), while abnormal total lung capacity (TLC) and residual volume (RV) were more frequent in SPs than in MPs (TLC, 18.8 vs. 8.3%; RV, 11.8 vs. 0%).Conclusions: Pulmonary abnormalities remained after recovery from COVID-19 and were more frequent and conspicuous in SPs at 3 months after discharge.

Optical‐resolution photoacoustic microscopy (OR‐PAM) technique is a noninvasive imaging technique that can be used to obtain high‐resolution images. This technique does not require the use of exogenous contrast agents. The technique can be effectively used to realize endogenous and exogenous imaging of microvasculature. However, the depth of focus (DOF) realized using OR‐PAM is low and inadequate to cover the entire 3D data for microvasculature imaging. A varying extent of signal‐to‐noise ratio (SNR) is recorded when varying depths of the 3D sample are studied. This significantly hindered the processes of image recognition, segmentation, and analysis. Herein, a deep learning‐based OR‐PAM technique to image and analyze 3D datasets is proposed. Endogenous and exogenous multi‐organ imaging data are sequentially imaged and segmented, and excellent generalization ability is observed. Wide‐field and ultradense exogenous 3D imaging of mouse brain vasculature located at different depths is obtained and segmented by OR‐PAM for the first time. The results reveal that the proposed method can be used for effectively imaging the entire microvasculature. The method can be potentially used for imaging the microcirculation system in clinics.

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