Comparison of lung image quality between CT Ark and Brilliance 64 CT during COVID-19.

AIM: This study is to compare the lung image quality between shelter hospital CT (CT Ark) and ordinary CT scans (Brilliance 64) scans. METHODS: The patients who received scans with CT Ark or Brilliance 64 CT were enrolled. Their lung images were divided into two groups according to the scanner. The objective evaluation methods of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used. The subjective evaluation methods including the evaluation of the fine structure under the lung window and the evaluation of the general structure under the mediastinum window were compared. Kappa method was used to assess the reliability of the subjective evaluation. The subjective evaluation results were analyzed using the Wilcoxon rank sum test. SNR and CNR were tested using independent sample t tests. RESULTS: There was no statistical difference in somatotype of enrolled subjects. The Kappa value between the two observers was between 0.68 and 0.81, indicating good consistency. For subjective evaluation results, the rank sum test P value of fine structure evaluation and general structure evaluation by the two observers was ≥ 0.05. For objective evaluation results, SNR and CNR between the two CT scanners were significantly different (P<0.05). Notably, the absolute values ​​of SNR and CNR of the CT Ark were larger than Brilliance 64 CT scanner. CONCLUSION: CT Ark is fully capable of scanning the lungs of the COVID-19 patients during the epidemic in the shelter hospital.

Contamination inside CT gantry in the SARS-CoV-2 era.

We investigated whether the internal gantry components of our computed tomography (CT) scanner contain severe acute respiratory syndrome 2 (SARS-CoV-2) ribonucleic acid (RNA), bacterial or fungal agents. From 1 to 27 March 2020, we performed 180 examinations of patients with confirmed SARS-CoV-2 infection using a dedicated CT scanner. On 27 March 2020, this CT gantry was opened and sampled in each of the following components: (a) gantry case; (b) inward airflow filter; (c) gantry motor; (d) x-ray tube; (e) outflow fan; (f) fan grid; (g) detectors; and (h) x-ray tube filter. To detect SARS-CoV-2 RNA, samples were analysed using reverse transcriptase-polymerase chain reaction (RT-PCR). To detect bacterial or fungal agents, samples have been collected using "replicate organism detection and counting" contact plates of 24 cm2, containing tryptic soy agar, and subsequently cultured. RT-PCR detected SARS-CoV-2 RNA in the inward airflow filter sample. RT-PCR of remaining gantry samples did not reveal the presence of SARS-CoV-2 RNA. Neither bacterial nor fungal agents grew in the agar-based growth medium after the incubation period. Our data showed that SARS-Cov-2 RNA can be found inside the CT gantry only in the inward airflow filter. All remaining CT gantry components were devoid of SARS-CoV-2 RNA.

Ultraviolet germicidal irradiation of the inner bore of a CT gantry.

PURPOSE: To investigate the feasibility and practicality of ultraviolet (UV) germicidal irradiation of the inner bore of a computed tomography (CT) gantry as a means of viral decontamination. METHOD: A UV lamp (PADNUT 38 W, 253 nm UV-C light tube) and UV-C dosimeter (GENERAL UV-C Digital Light Meter No. UV512C) were used to measure irradiance throughout the inner bore of a CT scanner gantry. Irradiance (units µW/cm2 ) was related to the time required to achieve 6-log viral kill (10-6 survival fraction). RESULTS: A warm-up time of ~120 s was required for the lamp to reach stable irradiance. Irradiance at the scan plane (z = 0 cm) of the CT scanner was 580.9 µW/cm2 , reducing to ~350 µW/cm2 at z = ±20 cm toward the front or back of the gantry. The angular distribution of irradiation was uniform within 10% coefficient of variation. A conservative estimate suggests at least 6-log kill (survival fraction ≤ 10-6 ) of viral RNA within ±20 cm of the scan plane with an irradiation time of 120 s from cold start. More conservatively, running the lamp for 180 s (3 min) or 300 s (5 min) from cold start is estimated to yield survival fraction <<10-7 survival fraction within ±20 cm of the scan plane. CONCLUSION: Ultraviolet irradiation of the inner bore of the CT gantry can be achieved with a simple UV-C lamp attached to the CT couch. Such practice could augment manual wipe-down procedures, improve safety for CT technologists or housekeeping staff, and could potentially reduce turnover time between scanning sessions.

Electrical impedance tomography to titrate positive end-expiratory pressure in COVID-19 acute respiratory distress syndrome.

RATIONALE: Patients with coronavirus disease-19-related acute respiratory distress syndrome (C-ARDS) could have a specific physiological phenotype as compared with those affected by ARDS from other causes (NC-ARDS). OBJECTIVES: To describe the effect of positive end-expiratory pressure (PEEP) on respiratory mechanics in C-ARDS patients in supine and prone position, and as compared to NC-ARDS. The primary endpoint was the best PEEP defined as the smallest sum of hyperdistension and collapse. METHODS: Seventeen patients with moderate-to-severe C-ARDS were monitored by electrical impedance tomography (EIT) and evaluated during PEEP titration in supine (n = 17) and prone (n = 14) position and compared with 13 NC-ARDS patients investigated by EIT in our department before the COVID-19 pandemic. RESULTS: As compared with NC-ARDS, C-ARDS exhibited a higher median best PEEP (defined using EIT as the smallest sum of hyperdistension and collapse, 12 [9, 12] vs. 9 [6, 9] cmH2O, p < 0.01), more collapse at low PEEP, and less hyperdistension at high PEEP. The median value of the best PEEP was similar in C-ARDS in supine and prone position: 12 [9, 12] vs. 12 [10, 15] cmH2O, p = 0.59. The response to PEEP was also similar in C-ARDS patients with higher vs. lower respiratory system compliance. CONCLUSION: An intermediate PEEP level seems appropriate in half of our C-ARDS patients. There is no solid evidence that compliance at low PEEP could predict the response to PEEP.

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.

BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity (0-3) and geographic extent (0-4). Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0-6) with a standard deviation of 0.25 (9.9%) across three readers. The average geographic extent score was 3.42 (range: 0-8) with a standard deviation of 0.57 (16.7%) across three readers. The inter-rater agreement yielded a Fleiss' Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R2) of 0.90 (range: 0.73-0.90 for traditional learning and 0.83-0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2-21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation.

Preparation of a radiology department in an Italian hospital dedicated to COVID-19 patients.

Preparedness for the ongoing coronavirus disease 2019 (COVID-19) and its spread in Italy called for setting up of adequately equipped and dedicated health facilities to manage sick patients while protecting healthcare workers, uninfected patients, and the community. In our country, in a short time span, the demand for critical care beds exceeded supply. A new sequestered hospital completely dedicated to intensive care (IC) for isolated COVID-19 patients needed to be designed, constructed, and deployed. Along with this new initiative, the new concept of "Pandemic Radiology Unit" was implemented as a practical solution to the emerging crisis, born out of a critical and urgent acute need. The present article describes logistics, planning, and practical design issues for such a pandemic radiology and critical care unit (e.g., space, infection control, safety of healthcare workers, etc.) adopted in the IC Hospital Unit for the care and management of COVID-19 patients.

Use of a portable computed tomography scanner for chest imaging of COVID-19 patients in the urgent care at a tertiary cancer center.

To present a novel use of a portable computed tomography (CT) for evaluation of COVID-19 patients presenting to an urgent care center (UCC). Infection control is imperative for hospitals treating patients with COVID-19, even more so in cancer centers, where the majority of the patient population is susceptible to adverse outcomes from the infection. Over the past several weeks, our department has worked to repurpose a portable CT scanner from our surgical colleagues that operates with fixed-parameters to perform non-contrast, helical, thin-slice chest imaging to address the known pulmonary complications of COVID-19. Despite the technical limitations of the portable CT unit that was repurposed for the UCC, diagnostic-quality images in an acute care setting were successfully obtained. Repurposing of a portable CT scanner for use in COVID-19 patients offers a feasible option to obtain diagnostic quality images while minimizing the risk of exposing other patients and hospital staff to an infected patient.

Diagnostic methods and potential portable biosensors for coronavirus disease 2019.

Timely detection and diagnosis are urgently needed to guide epidemiological measures, infection control, antiviral treatment, and vaccine research. In this review, biomarkers/indicators for diagnosis of coronavirus disease 2019 (COVID-19) or detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the environment are summarized and discussed. It is concluded that the detection methods targeting antibodies are not suitable for screening of early and asymptomatic cases since most patients had an antibody response at about 10 days after onset of symptoms. However, antibody detection methods can be combined with quantitative real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) to significantly improve the sensitivity and specificity of diagnosis, and boost vaccine research. Fast, sensitive and accurate detection methods targeting antigens need to be developed urgently. Various specimens for diagnosis or detection are compared and analyzed. Among them, deep throat saliva and induced sputum are desired for RT-qPCR test or other early detection technologies. Chest computerized tomography (CT) scan, RT-qPCR, lateral flow immunochromatographic strip (LFICS) for diagnosis of COVID-19 are summarized and compared. Specially, potential electrochemical (EC) biosensor, surface enhanced Raman scattering (SERS)-based biosensor, field-effect transistor (FET)-based biosensor, surface plasmon resonance (SPR)-based biosensor and artificial intelligence (AI) assisted diagnosis of COVID-19 are emphasized. Finally, some commercialized portable detection device, current challenges and future directions are discussed.

Disposable Isolation Device to Reduce COVID-19 Contamination During CT Scanning.

RATIONALE AND OBJECTIVES: The use of chest computed tomography (CT) in the era of the COVID-19 pandemic raises concern regarding the transmission risks to patients and staff caused by CT room contamination. Meanwhile the Center for Disease Control guidance for air exchange in between patients may heavily impact workflows. To design a portable custom isolation device to reduce imaging equipment contamination during a pandemic. MATERIALS AND METHODS: Center for Disease Control air exchange guidelines and requirements were reviewed. Device functional requirements were outlined and designed. Engineering requirements were reviewed. Methods of practice and risk mitigation plans were outlined including donning and doffing procedures and failure modes. Cost impact was assessed in terms of CT patient throughput. RESULTS: CT air exchange solutions and alternatives were reviewed. Multiple isolation bag device designs were considered. Several designs were custom fabricated, prototyped and reduced to practice. A final design was tested on volunteers for comfort, test-fit, air seal, and breathability. Less than 14 times enhanced patient throughput was estimated, in an ideal setting, which could more than counterbalance the cost of the device itself. CONCLUSION: A novel isolation bag device is feasible for use in CT and might facilitate containment and reduce contamination in radiology departments during the COVID Pandemic.

Infection Control for CT Equipment and Radiographers' Personal Protection During the Coronavirus Disease (COVID-19) Outbreak in China.

OBJECTIVE. Because CT plays an important role in diagnosis, isolation, treatment, and effective evaluation of coronavirus disease (COVID-19), infection prevention and control management of CT examination rooms is important. CONCLUSION. We describe modifications to the CT examination process, strict disinfection of examination rooms, arrangement of waiting areas, and efforts to increase radiographers' awareness of personal protection made at our institution during the COVID-19 outbreak. In addition, we discuss the potential of using artificial intelligence in imaging patients with contagious diseases.

COVID-19 pneumonia: infection control protocol inside computed tomography suites.

A novel coronavirus (severe acute respiratory syndrome coronavirus 2) causes a cluster of pneumonia cases in Wuhan, China. It spread rapidly and globally. CT imaging is helpful for the evaluation of the novel coronavirus disease 2019 (COVID-19) pneumonia. Infection control inside the CT suites is also important to prevent hospital-related transmission of COVID-19. We present our experience with infection control protocol for COVID-19 inside the CT suites.