Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology.

BACKGROUND: The ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging . METHODS: From February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups. RESULTS: Compared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001). CONCLUSIONS: The automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.

OBJECTIVE: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND METHODS: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. RESULTS: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. CONCLUSION: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. KEY POINTS: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

Features of family clusters of COVID-19 patients: A retrospective study.

BACKGROUND: To investigate and compare the clinical and imaging features among family members infected with COVID-19. METHODS: We retrospectively collected a total of 34 COVID-19 cases (15 male, 19 female, aged 48 ± 16 years, ranging from 10 to 81 years) from 13 families from January 17, 2020 through February 15, 2020. Patients were divided into two groups: Group 1 - part of the family members (first-generation) who had exposure history and others (second-generation) infected through them, and Group 2 - patients from the same family having identical exposure history. We collected clinical symptoms, laboratory findings, and high-resolution computed tomography (HRCT) features for each patient. Comparison tests were performed between the first- and second-generation patients in Group 1. RESULTS: In total there were 21 patients in Group 1 and 20 patients in Group 2. For Group 1, first-generation patients had significantly higher white blood cell count (6.5 × 109/L (interquartile range (IQR): 4.9-9.2 × 109/L) vs 4.5 × 109/L (IQR: 3.7-5.3 × 109/L); P = 0.0265), higher neutrophil count (4.9 × 109/L (IQR: 3.6-7.3 × 109/L) vs 2.9 × 109/L (IQR: 2.1-3.3 × 109/L); P = 0.0111), and higher severity scores on HRCT (3.9 ± 2.4 vs 2.0 ± 1.3, P = 0.0362) than the second-generation patients. Associated underlying diseases (odds ratio, 8.0, 95% confidence interval: 3.4-18.7, P = 0.0013) were significantly correlated with radiologic severity scores in second-generation patients. CONCLUSION: Analysis of the family cluster cases suggests that COVID-19 had no age or sex predominance. Secondarily infected patients in a family tended to develop milder illness, but this was not true for those with existing comorbidities.

Prominent Hypercoagulability Associated With Inflammatory State Among Cancer Patients With SARS-CoV-2 Infection.

Abnormal coagulation parameters and potential benefits of anticoagulant therapy in general population with novel coronavirus pneumonia (COVID-19) have been reported. However, limited data are available on cancer patients. Coagulation indexes and inflammation parameters in 57 cancer patients with SARS-CoV-2 infection with different severity were retrospectively analyzed. We found that D-dimer levels were increased in 33 patients (57.9%, median: 790 ng/mL). Compared with ordinary type patients, severe and critical ill patients had decreased MPV values (P = 0.006), prolonged PT (median: 13.3 vs. 11.5 vs. 11.4 s, P < 0.001), significant higher D-dimer levels (median: 2,400 vs. 940 vs. 280 ng/mL, P < 0.001), higher PCT levels (median: 0.17 vs. 0.055 vs. 0.045 ng/mL, P = 0.002), higher IL-6 (median: 20.6 vs. 2.3 vs. 3.0 pg/mL, P = 0.040), and decreased PaO2 (median: 68 vs. 84 vs. 96 mm Hg, P < 0.001). Importantly, three patients, one severe and two critical ill type, with increased D-dimer survived after anticoagulant therapy with continuous heparin infusion. Increased D-dimer levels positively correlated with increased PCT levels (r = 0.456, P = 0.002) and IL-6 levels (r = 0.501, P = 0.045). A negative correlation between D-dimer levels and PaO2 levels (r = -0.654, P = 0.021) were also existed. Cancer patients with COVID-19 showed prominent hypercoagulability associated with severe inflammation, anticoagulation therapy might be useful to improve the prognosis and should be immediately used after the onset of hypercoagulability.

A fatal outcome from SARS-CoV-2 infection: One case report of a young man with multiple organ damage.

The coronavirus disease 2019 (COVID-19) first emerged in Wuhan, China on December 2019 and has become a severe public health issue worldwide. A 36-year-old man was presented to the hospital staff with a fever that had already persisted for a three-day period, general weakness and diarrhea. He had no chronic diseases and was tested positive for COVID-19 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid. During his hospitalization, several abnormal indicators appeared in his laboratory tests, which implied systemic inflammation and multiple organ damage. A series of chest radiographs monitored the dynamic process of lung lesions, which could predict the clinical changes of the patient. His condition deteriorated rapidly, resulting in death due to acute respiratory distress syndrome (ARDS) on hospital day 13. The case indicates that inflammatory response may appear in people infected with SARS-CoV-2 and may lead to multiple organ damage (especially pancreatic damage). When a COVID-19 patient is entering into the critical stage, their condition could rapidly deteriorate.