Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues.

PURPOSE: Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. METHODS: With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). RESULTS: The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11-40% relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027-0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). CONCLUSIONS: Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses.

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

Role of low field MRI in detecting knee lesions.

Objective The aim of this work is to evaluate the diagnostic accuracy of 0.3T sectoral MR imaging, compared with arthroscopy, for meniscal, cruciate ligaments and chondral knee lesions. Materials and Methods We conducted a retrospective study analyzing all the consecutive knees subjected to arthroscopy at our institution between January 2014 and June 2017 and preceded within 3 months by knee MR examination at our institution with 0.3 T equipment. Patients with history of a new trauma in the time interval between MR exam and arthroscopy were excluded from the study. Two independent experienced radiologists evaluated in double blind the MR findings of menisci, cruciate ligaments and articular cartilage. Both radiological findings were independently compared with those of the arthroscopic report considered as gold standard. For each of the examined targets we calculated the following parameters: sensitivity, specificity, accuracy, positive and negative predictive value; interobserver concordance statistically calculated using Cohen's Kappa test. Results 214 knees (95R/119L) of 214 patients (143M/71F) aged from 18 to 72 years (mean 44) were included and analyzed. We found a good diagnostic accuracy of the low field MR in identifying the injuries of the menisci (93%) and the crossed ligaments (96%), but a lower accuracy for the articular cartilage (85%). Sensitivity resulted 90% for menisci, 73% for ligaments and 58% for cartilage. Specificity was 91% for menisci, 97% for ligaments and 92% for cartilage. Inter-observer concordance resulted to be excellent for cruciate ligaments (K of Cohen's test = 0.832), good (K = 0.768) for menisci, modest to moderate for articular cartilage (K from 0.236 to 0.389) with worse concordance for tibial cartilage. Conclusions Low-field MR sectoral device with dedicated joint equipment confirms its diagnostic reliability for the evaluation of meniscal and cruciate ligaments lesions but is weak in evaluating low grade chondral lesions.