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1.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.06.04.20119206

ABSTRACT

Background: The corona-virus disease 2019 (COVID-19) pandemic has caused a serious public health risk. Compared with conventional high-resolution CT (C-HRCT, matrix 512), ultra-high resolution CT (U-HRCT, matrix 1024) can increase the effective pixel per unit volume by about 4 times. Our study is to evaluate the value of target reconstruction of U-HRCT in the accurate diagnosis of COVID-19. Methods: A total of 13 COVID-19 cases, 44 cases of other pneumonias, and 6 cases of ground-glass nodules were retrospectively analyzed. The data were categorized into groups A (C-HRCT) and B (U-HRCT), following which iDose4-3 and iDose4-5 were used for target reconstruction, respectively. CT value, noise, and signal-to-noise ratio (SNR) in different reconstructed images were measured. Two senior imaging doctors scored the image quality and the structure of the lesions on a 5-point scale. Chi-square test, variance analysis, and binarylogistic regression analysis were used for statistical analysis. Results: U-HRCT image can reduce noise and improve SNR with an increase of the iterative reconstruction level. The SNR of U-HRCT image was lower than that of the C-HRCT image of the same iDose4level, and the noise of U-HRCT was higher than that of C-HRCT image; the difference was statistically significant (P< 0.05). Logistic regression analysis showed thatperipleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern were independent indictors of the COVID-19 on U-HRCT. U-HRCT was superior to C-HRCT in showing the blood vessels, bronchial wall, and interlobular septum in the ground-glass opacities; the difference was statistically significant (P < 0.05). Conclusions:Peripleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern on U-HRCT are favorable signs for COVID-19. U-HRCT is superior to C-HRCT in displaying the blood vessels, bronchial walls, and interlobular septum for evaluating COVID-19. Keywords: U-HRCT, 1024 matrix, Target Reconstruction, COVID-19


Subject(s)
COVID-19 , Pneumonia
2.
researchsquare; 2020.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-28296.v1

ABSTRACT

Background: The corona-virus disease 2019 (COVID-19) pandemic has caused a serious public health risk. Compared with conventional high-resolution CT (C-HRCT, matrix 512), ultra-high resolution CT (U-HRCT, matrix 1024) can increase the effective pixel per unit volume by about 4 times. Our study is to evaluate the value of target reconstruction of U-HRCT in the accurate diagnosis of COVID-19. Methods: A total of 13 COVID-19 cases, 44 cases of other pneumonias, and 6 cases of ground-glass nodules were retrospectively analyzed. The data were categorized into groups A (C-HRCT) and B (U-HRCT), following which iDose4-3 and iDose4-5 were used for target reconstruction, respectively. CT value, noise, and signal-to-noise ratio (SNR) in different reconstructed images were measured. Two senior imaging doctors scored the image quality and the structure of the lesions on a 5-point scale. Chi-square test, variance analysis, and binary logistic regression analysis were used for statistical analysis. Results: U-HRCT image can reduce noise and improve SNR with an increase of the iterative reconstruction level. The SNR of U-HRCT image was lower than that of the C-HRCT image of the same iDose4 level, and the noise of U-HRCT was higher than that of C-HRCT image; the difference was statistically significant (P < 0.05). Logistic regression analysis showed that peripleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern were independent indictors of the COVID-19 on U-HRCT. U-HRCT was superior to C-HRCT in showing the blood vessels, bronchial wall, and interlobular septum in the ground-glass opacities; the difference was statistically significant (P < 0.05). Conclusions: Peripleural distribution, thickening of blood vessels and interlobular septum, and crazy-paving pattern on U-HRCT are favorable signs for COVID-19. U-HRCT is superior to C-HRCT in displaying the blood vessels, bronchial walls, and interlobular septum for evaluating COVID-19.


Subject(s)
COVID-19 , Pneumonia
3.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.03.31.20048256

ABSTRACT

Background: An ongoing outbreak of mystery pneumonia in Wuhan was caused by coronavirus disease 2019 (COVID-19). The infectious disease has spread globally and become a major threat to public health. Purpose: We aim to investigate the ultra-high-resolution CT (UHR-CT) findings of imported COVID-19 related pneumonia from the initial diagnosis to early-phase follow-up. Methods: This retrospective study included confirmed cases with early-stage COVID-19 related pneumonia imported from the epicenter. Initial and early-phase follow-up UHR-CT scans (within 5 days) were reviewed for characterizing the radiological findings. The normalized total volumes of ground-glass opacities (GGOs) and consolidations were calculated and compared during the radiological follow-up by artificial-intelligence-based methods. Results: Eleven patients (3 males and 8 females, aged 32-74 years) with confirmed COVID-19 were evaluated. Subpleural GGOs with inter/intralobular septal thickening were typical imaging findings. Other diagnostic CT features included distinct margins (8/11, 73%), pleural retraction or thickening (7/11, 64%), intralesional vasodilatation (6/11, 55%). Normalized volumes of pulmonary GGOs (p=0.003) and consolidations (p=0.003) significantly increased during the CT follow-up. Conclusions: The abnormalities of GGOs with peripleural distribution, consolidated areas, septal thickening, pleural involvement and intralesional vasodilatation on UHR-CT indicate the diagnosis of COVID-19. COVID-19 cases could manifest significantly progressed GGOs and consolidations with increased volume during the early-phase CT follow-up.


Subject(s)
Porcine Reproductive and Respiratory Syndrome , Pleural Diseases , Pneumonia , Communicable Diseases , COVID-19
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