Objective:To explore the value of low-dose CT in pregnancy with COVID-19.Methods:A retrospective analysis was performed on the clinical characteristics, laboratory tests, and chest CT findings of 12 pregnant women with COVID-19 diagnosed by nucleic acid testing in the Renmin Hospital of Wuhan University from January 20, 2020 to February 16, 2020. Two radiologists blinded to the reconstruction algorithm independently scored subjective image quality on a 5-point Likert scale. Image quality score ≥ 3 was acceptable in clinics. The CT radiation doses were recorded, including CT volume dose index (CTDI vol), dose length product (DLP), and effective radiation dose ( E). Two radiologists observed the distribution, shape, density, and other characteristics of lung lesions, and they also decided whether hilar, mediastinal lymphadenopathy, and pleural changed. Results:A total of 12 pregnant women with COVID-19, 8 had cough, 4 had fever, 2 had chest tightness, and 1 had dyspnea and diarrhea each. The CT image quality score of all patients was 3-4, with an average of 3.46, which fully met the clinical diagnosis requirements. The CTDI vol value was 1.13-4.31 mGy, with an average of 3.02 mGy. The DLP value was 34.48-75.29 mGy·cm, with an average of 55.48 mGy·cm. The Evalue was 0.48-1.05 mSv, with an average of 0.78 mSv. In all cases, chest CT examination showed abnormal manifestations after clinical symptoms, including unilateral lung lesions in 5 cases and bilateral lung lesions in 7 cases, 1 case of ground-glass opacity, 1 case of solidification, 7 cases of ground-glass and consolidation, 1 case of strip opacity, ground-glass, and consolidation and strip cable shadow coexisted in 2 cases. Conclusions:The application of low-dose CT scan in pregnant women with COVID-19 is completely feasible. CT mainly manifested as bilateral lung patchy and flaky ground-glass opacity with consolidation. Active and effective treatment can help recover and improve prognosis.

Objective:To explore the value of chest low-dose CT (LDCT) in post-discharge follow-up assessments of patients with coronavirus disease 2019 (COVID-19).Methods:The chest CT findings of 58 patients with COVID-19 from March 17 to March 25, 2020 at Remin Hospital of Wuhan University were retrospectively analyzed. Two radiologists independently scored the subjective image quality on a 5-point Likert scale. The signal-to-noise ratio (SNR) and SD air of images and the CT radiation dose parameters were calculated, including the CT volume dose index (CTDI vol), dose length product (DLP), and effective radiation dose ( E). Results:The subjective image quality scores on CT images obtained before and after discharge by readers 1 and 2, were 4.45±0.22, 3.88±0.33 ( P>0.05) and 4.37±0.18, 3.91±0.35 ( P>0.05), respectively. The SNR and SD air in LDCT after discharge were 4.39±0.95 and 7.19±2.41, which were significantly lower than those in routine chest CT before discharge (5.14±1.06, Z=-5.551, P<0.001; 6.48±1.57, Z=-3.217, P<0.001). All of the obtained images were sufficient for diagnosis. The CTDI vol, DLP, and E in LDCT were significantly lower than those in routine CT [(2.41±0.09), (10.53±1.03)mGy, Z=-6.568, P<0.001; (88.03±5.33), (338.74±34.64)mGy·cm, Z=-6.624, P<0.001; and (1.23±0.17), (4.74±0.48)mSv, Z=-5.976, P<0.001]. Conclusions:Patients with COVID-19 can be followed up with low-dose chest CT after discharge.

Aiming at the non-stationarity and temporal characteristics of variable-length electrocardiogram(ECG)signals,an arrhythmia identification algorithm is proposed based on continuous wavelet transform and higher-order statistics.Considering the varying number of data points for each sample in variable-length ECG signals,the RR interval interpolation method is employed for data preprocessing,and the signal is decomposed into different time-frequency components using continuous wavelet transform,which enables the network to better extract both temporal and frequency features from the ECG signals.Regarding the issue of insufficient utilization of temporal information,a temporal mining module is introduced based on higher-order statistics and long short-term memory network to capture and learn long-term dependencies in the ECG signals,thereby facilitating the identification and understanding of specific arrhythmia categories.Extensive experiments conducted on the publicly available MIT-BIH ECG database validate the effectiveness and superiority of the proposed method.