Did radiation exposure increase with chest computed tomography use among different ages during the COVID-19 pandemic? A multi-center study with 42028 chest computed tomography scans

PURPOSETo determine whether radiation exposure increased among different ages with chest computed tomography (CT) use during the coronavirus disease-2019 (COVID-19) pandemic.METHODSPatients with chest CT scans in an 8-month period of the pandemic between March 15, 2020, and November 15, 2020, and the same period of the preceding year were included in the study. Indications of chest CT scans were obtained from the clinical notes and categorized as infectious diseases, neoplastic disorders, trauma, and other diseases. Chest CT scans for infectious diseases during the pandemic were compared with those with the same indications in 2019. The dose-length product values were obtained from the protocol screen individually.RESULTSThe total number of chest CT scans with an indication of infectious disease was 21746 in 2020 and 4318 in 2019. Total radiation exposure increased by 573% with the use of chest CT for infectious indications but decreased by 19% for neoplasia, 12% for trauma, and 43% for other reasons. The mean age of the patients scanned in 2019 was significantly higher than those scanned during the pandemic (64.6 vs. 50.3 years). A striking increase was seen in the 10–59 age group during the pandemic (P < 0.001). The highest increase was seen in the 20–29 age group, being 18.6 fold. One death was recorded per 58 chest CT scans during the pandemic. Chest CT use was substantially higher at the beginning of the pandemic.CONCLUSIONChest CT was excessively used during the COVID-19 pandemic. Young and middle-aged people were exposed more than others. The impact of COVID-19-pandemic-related radiation exposure on public health should be followed carefully in future years

Comparison of ASL and DSC perfusion methods in the evaluation of response to treatment in patients with a history of treatment for malignant brain tumor

Objective: Perfusion MRI is of great benefit in the post-treatment evaluation of brain tumors. Interestingly, dynamic susceptibility contrast-enhanced (DSC) perfusion has taken its place in routine examination for this purpose. The use of arterial spin labeling (ASL), a perfusion technique that does not require exogenous contrast material injection, has gained popularity in recent years. The aim of the study was to compare two different perfusion techniques, ASL and DSC, using qualitative and quantitative measurements and to investigate the diagnostic effectiveness of both. The fact that the number of patients is higher than in studies conducted with 3D pseudo-continious ASL (pCASL), the study group is heterogeneous as it consists of patients with both metastases and glial tumors, the use of 3D Turbo Gradient Spin Echo (TGSE), and the inclusion of visual (qualitative) assessment make our study unique. Methods: Ninety patients, who were treated for malignant brain tumor, were enrolled in the retrospective study. DSC Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF) and ASL CBF maps of each case were obtained. In qualitative analysis, the lesions of the cases were visually classified as treatment-related changes (TRC) and relapse/residual mass (RRT). In the quantitative analysis, three regions of interest (ROI) measurements were taken from each case. The average of these measurements was compared with the ROI taken from the contralateral white matter and normalized values (n) were obtained. These normalized values were compared across events. Results: Uncorrected DSC normalized CBV (nCBV), DSC normalized CBF (nCBF) and ASL nCBF values of RRT cases were higher than those of TRC cases (p < 0.001). DSC nCBV values were correlated with DSC nCBF (r: 0.94, p < 0.001) and correlated with ASL nCBF (r: 0.75, p < 0.001). Similarly, ASL nCBF was positively correlated with DSC nCBF (r: 0.79 p < 0.01). When the ROC curve parameters were evaluated, the cut-off values were determined as 1.211 for DSC nCBV (AUC: 0.95, 93% sensitivity, 82% specificity), 0.896 for DSC nCBF (AUC; 0.95, 93% sensitivity, 82% specificity), and 0.829 for ASL nCBF (AUC: 0.84, 78% sensitivity, 75% specificity). For qualitative evaluation (visual evaluation), inter-observer agreement was found to be good for ASL CBF (0.714), good for DSC CBF (0.790), and excellent for DSC CBV (0.822). Intra-observer agreement was also evaluated. For the first observer, good agreement was found in ASL CBF (0.626, 70% sensitive, 93% specific), in DSC CBF (0.713, 76% sensitive, 95% specific), and in DSC CBV (0.755, 87% sensitive - 88% specific). In the second observer, moderate agreement was found in ASL CBF (0.584, 61% sensitive, 97% specific) and DSC CBF (0.649, 65% sensitive, 100% specific), and excellent agreement in DSC CBV (0.800, 89% sensitive, 90% specific). Conclusion: It was observed that uncorrected DSC nCBV, DSC nCBF and ASL nCBF values were well correlated with each other. In qualitative evaluation, inter-observer and intra-observer agreement was higher in DSC CBV than DSC CBF and ASL CBF. In addition, DSC CBV is found more sensitive, ASL CBF and DSC CBF are found more specific for both observers. From a diagnostic perspective, all three parameters DSC CBV, DSC CBF and ASL CBF can be used, but it was observed that the highest rate belonged to DSC CBV

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies

Purpose: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. Materials and Methods: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels. U-Net based network was trained followed by majority voting of the voxel wise predictions of the model to transform them into patient level stroke territory classes. Effects of bias field correction and registration to a common space were explored. Results: Of the 218 patients included in this study, 141 (65%) were anterior stroke, and 77 were posterior stroke (35%) whereas 117 (53%) were male and 101 (47%) were female. The model built with original images reached 0.77 accuracy, while the model built with N4 bias corrected images reached 0.80 and the model built with images which were N4 bias corrected and then registered into a common space reached 0.83 accuracy values. Conclusion: Voxel wise dense prediction coupled with bias field correction to eliminate artificial signal increase and registration to a common space help models for better performance than using original images. Knowing the properties of target domain while designing deep learning models is important for the overall success of these models