• Korean Journal of Radiology
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• v.22 no.6
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

• Journal of the Korean Society of Radiology
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• v.82 no.6
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• pp.1505-1523
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• 2021

Purpose Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. Materials and Methods We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy. Results From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. Conclusion Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

• Journal of Chest Surgery
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• v.39 no.8 s.265
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• pp.573-578
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• 2006

Background: Generally hernia is diagnosed with simple chest or gastrointestinal x-ray. Sometimes CT or MRI can give lots of information for the diagnosis. However, there was no study for the differentiation with using CT findings between Morgagni hernia and pleuropericardial fat. The aim of this study was to evaluate the useful CT findings for differentiating Morgagni hernia from pleuropericardial fat. Material and Method: We retrospectively analyzed CT scans of eight patients with Morgagni hernia and 20 patients with abundant pleuropericardial fat without peridiaphragmatic lesions. All CT scans were performed with coverage of the whole diaphragm in the inspiration state. We evaluated 1) the presence of the defect of the anterior diaphragm, 2) the interface between the lung and fat, 3) the angle between the chest wall and fat, 4) the continuity between the extrapleural fat and fat, 5) the presence of the vessels within fat, and 6) the presence of a thin line surrounding fat. Result: In all cases with Morgagni hernia, the defect of the anterior diaphragm was seen. The interface was well-defined, smooth, and convex to the lung. The angle with the chest wall was acute. The continuity with the extrapleural fat was not seen. In the cases with abundant pleuropericardial fat, the defect of the anterior diaphragm was seen in three (15%). The interface was usually irregular (n=10) and flat (n=17). The angle with the chest wall was variable. The continuity with the extrapleural fat, that was markedly increased in amount, was usually seen (n=16). The thin line surrounding fat was seen in four cases with Morgagni hernia, however, not seen in all cases with pleuropericardial fat. All of the above findings were statistically significant, however, vessels within fat was not significant to differentiate Morgagni hernia (n=8/8) from pleuropericardial fat (n=14/20). Conclusion: The useful CT findings of Morgagni hernia were fatty mass with sharp margin, convexity toward lung, acute angle with chest wall, and thin line surrounding hernia. Branching structure within fatty mass representing omental vessels that has been known as a characteristic finding of Morgagni hernia was not useful for differentiating Morgagni hernia from pleuropericardial fat.

• Journal of Broadcast Engineering
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• v.18 no.5
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• pp.702-712
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• 2013

In this paper, we proposed an algorithm that extracts the airway and lung without loss of information in spite of the pulmonary vessel and nodules of the chest wall in the chest CT images. We use a mask image in order to improve the performance and to save processing time of airway and lung segmentation. In the second step, by converting left and right lungs to binary image using the morphological information, we have removed the solitary pulmonary nodule to identify the value of the threshold lung and the chest wall. The last step is to connect the outer shell of the lung with cubic Spline interpolation by adding the perfect pixel and computing the distance of the removed part. Experimental results using Matlab verified that the proposed method could overcome the drawbacks of the conventional methods.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.285-295
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• 2023

With the development of technology, efforts to reduce the exposure dose received by patients in CT scans are continuing with the development of new reconstruction techniques. Recently, deep learning reconstruction techniques have been developed to overcome the limitations of repetitive reconstruction techniques. This study aims to evaluate the usefulness of images according to reconstruction techniques in pediatric chest CT images. Patient study conducted a study on 85 pediatric patients who underwent chest CT scan at P-Hospital in Gyeongsangnam-do from January 1, 2021 to December 31, 2022. The phantom used in the Phantom Study is the Pediatrics Whole Body Phantom PBU-70. After the test, the images were reconstructed with FBP, ASIR-V (50%) and DLIR (TF-Medium, High), and the images were evaluated by obtaining SNR and CNR values by setting ROI of the same size. As a result, TF-H of deep learning reconstruction techniques had the lowest noise value compared to ASIR-V (50%) and TF-M in all experiments, and SNR and CNR had the highest values. In pediatric chest CT scans, TF images with deep learning reconstruction techniques were less noisy than ASiR-V images with adaptive statistical iterative reconstruction techniques, CNR and SNR were higher, and the quality of images was improved compared to conventional reconstruction techniques.

• Journal of Trauma and Injury
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• v.33 no.2
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• pp.88-95
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• 2020

Purpose: The present study will identify risk factors for aspiration in severe trauma patients by comparing patients who showed a sign of aspiration lung disease on chest computed tomography (CT) and those who did not. Methods: We conducted a retrospective review of the Korean Trauma Data Bank between January 2014 and December 2019 in a single regional trauma center. The inclusion criteria were patients aged ≥18 years with chest CT, and who had an Injury Severity Score ≥16. Patients with Abbreviated Injury Scale (AIS)-chest score ≥1 and lack of medical records were excluded. General characteristics and patient status were analyzed. Results: 425 patients were included in the final analysis. There were 48 patients showing aspiration on CT (11.2%) and 377 patients showing no aspiration (88.7%). Aspiration group showed more endotracheal intubation in the ER (p=0.000) and a significantly higher proportion of severe Glasgow Coma Scale (GCS) (p=0.000) patients than the non-aspiration group. In AIS as well, the median AIS head score was higher in the aspiration group (p=0.046). Median oxygen saturation was significantly lower in the aspiration group (p=0.002). In a logistic regression analysis, relative to the GCS mild group, the moderate group showed an odds ratio (OR) for aspiration of 2.976 (CI, 1.024-8.647), and the severe group showed an OR of 5.073 (CI, 2.442-10.539). Conclusions: Poor mental state and head injury increase the risk of aspiration. To confirm for aspiration, it would be useful to perform chest CT for severe trauma patients with a head injury.

• Journal of Trauma and Injury
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• v.33 no.2
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• pp.119-123
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• 2020

The Coronavirus disease 2019 (COVID-19) has rapidly spread across the world and caused a pandemic. It can be transmitted by an infected person or an asymptomatic carrier and is a highly contagious disease. Prevention and early identification of COVID-19 are important to minimize the transmission of COVID-19. Chest computed tomography (CT) has a high sensitivity for detecting COVID-19, but relatively low specificity. Therefore, chest CT may be difficult to distinguish COVID-19 findings from those of other infectious (notably viral types of pneumonia) or noninfectious disease. Pulmonary contusion has also a lot of similarities on chest CT with COVID-19 pneumonia. We present trauma patients with pulmonary contusion whose CT scans showed findings similar to those of COVID-19, and we report our experience in the management of trauma patients during the COVID-19 pandemic.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.994-1003
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• 2017

Ground-glass opacity nodules(GGNs) in chest CT images are associated with lung cancer, and have a different malignant rate depending on existence of solid component in the nodules. In this paper, we propose a method to classify pure GGNs and part-solid GGNs using multiview images and texture analysis in pulmonary GGNs with solid components of 5mm or smaller. We extracted 1521 features from the GGNs segmented from the chest CT images and classified the GGNs using a SVM classification model with selected features that classify pure GGNs and part-solid GGNs through a feature selection method. Our method showed 85% accuracy using the SVM classifier with the top 10 features selected in the multiview images.

• Journal of the Korean Society of Radiology
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• v.79 no.6
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• pp.303-310
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• 2018

Purpose: To evaluate the CT features of incidental breast lesions on chest CT and to suggest useful criteria for referral to a specialized breast unit. Materials and Methods: Between May 2009 and April 2014, enhanced chest CT examination reports containing the key word 'breast' were reviewed retrospectively. Patients who had incidental breast lesion and were referred to a specialized breast unit and then underwent pathological confirmation or follow-up over a 1-year period were included. Finally, 86 patients (all female, mean age, $48.9{\pm}12.6years$) were enrolled. Two radiologists evaluated lesion characteristics, including size, shape, margins, and enhancement. The correlations between the CT features and pathologies were evaluated, and the diagnostic accuracy of CT features in various combinations was assessed. Results: Among the CT features, irregular shape, non-circumscribed margin, and high contrast enhancement were different between malignant and benign lesions (p < 0.05). The combination of non-circumscribed margin and high contrast enhancement had the highest accuracy (97.7%). Conclusion: Reliable CT features for incidental malignant breast masses are irregular shape, non-circumscribed margin, and high contrast enhancement. The combination of non-circumscribed margin and high contrast enhancement could help distinguish incidental malignant breast lesions and indicate referral to a specialized breast unit.

• 대한핵의학회:학술대회논문집
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• 2002.05a
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• pp.25-27
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• 2002

With the advent of multidetector-row CT, lung cancer imaging is much more promising than before. However, the effectiveness of multidetector-row CT in making an initial diagnosis, staging, and evaluating post-treatment changes of lung cancer still remains to be proved. Fast imaging along with volumetric data set and attendant multi-planar imaging provide much more details on the anatomic changes and pathology associated with lung cancer. However, with images showing anatomic and pathologic changes only, radiologists confront with several questions the answers of which may help evaluate lung cancer more thoroughly. The frequent questions that I have in dally practice of chest CT interpretation are as follows.