• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.324-336
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• 2020

Cardiovascular calcifications can occur in various cardiovascular diseases and can serve as a biomarker for cardiovascular event prediction. Advances in CT have enabled evaluation of calcifications in cardiovascular structures not only on ECG-gated CT but also on non-ECG-gated CT. Therefore, many studies have been conducted on the clinical relevance of cardiovascular calcifications in patients. In this study, we divided cardiovascular calcifications into three classes, i.e., coronary artery, thoracic aorta, and cardiac valve calcifications, which are closely associated with cardiovascular events. Further, we briefly described pericardial calcifications, which can be found incidentally. Since the start of lung cancer screening in Korea in the second half of 2019, the number of non-enhanced, non-ECG-gated, low-dose chest CT has been increasing, and the number of incidentally found cardiovascular calcifications has also been increasing. Therefore, understanding the relevance of cardiovascular calcifications on non-enhanced, non-ECG-gated, low-dose chest CT and their proper reporting are important for radiologists.

• Tuberculosis and Respiratory Diseases
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• v.62 no.1
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• pp.19-26
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• 2007

Background: Toxocariasis is a common cause of eosinophilia and eosinophilic lung disease in Korea. We analyzed the characteristics of eosinophilic lung disease in toxocariasis. Method: One hundred and forty one patients with eosinophilia caused by a toxocara larval infection were evaluated from September 1, 2001 through March 30, 2006. The plain chest x-ray, chest CT(computed tomography), and bronchoalveolar larvage(BAL) were examined. A diagnosis of toxocariasis was made by ELISA using that secretory-excretory antigen from the T. canis larvae. Results: Toxocarial eosinophilic lung diseases was diagnosed in 32 out of 141 patients. Ground glass attenuation was the main feature on the CT scans in 23 out of 141 patients (71.9%). Thirteen patients (40.6%) had a random in zonal distribution on CT. Pleural effusion was observed in 9 patients (28.1%). Twenty eight patients (87.5%) complained of respiratory symptoms. Eleven patients (34.4%) had gastrointestinal symptoms and 12 patients (37.5%) had liver infiltration. Conclusions: The most common findings of the chest CT in patients with toxocariasis was a randomly distributed ground grass attenuation. A toxocara infection should be considered in a differential diagnosis of patients who exhibit pulmonary infiltration with eosinophilia in Korea.

• Journal of the Korean Society of Radiology
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• v.82 no.2
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• pp.359-370
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• 2021

Purpose To analyze the findings and serial changes in chest CT lesions in 123 symptomatic patients with coronavirus disease 2019 (COVID-19). Materials and Methods From February 19 to April 7, 2020, a total of 123 confirmed COVID-19 patients (male, 44; female, 79; mean age, 59.2 ± 18.6) were enrolled in this retrospective study. A total of 234 CT scans were reviewed for the following patterns: acute alveolar insult (AAI) patterns: ground-glass opacity (GGO), crazy-paving appearance, mixed pattern, and consolidation; organizing pneumonia (OP) patterns: perilobular patterns, band opacity, curvilinear opacity, reversed halo opacity, and small nodular consolidation; resolving patterns: pure GGO, remnant curvilinear, small nodular consolidation, and serial changes of lung abnormalities. We compared the proportions of AAI pattern, OP pattern, or resolving pattern with time progression and analyzed the association between the patterns and disease severity using Pearson chi-square and Fisher's exact test. Results Predominant CT patterns were AAI pattern (87%) in the early hospital period group (0-10 days, after the onset of symptoms), OP pattern (45.7%) in the later hospital period group (after 10 days), and resolving pattern in discharge and follow-up group (47.2% and 84.8%, respectively). The difference in the proportions of predominant CT patterns with time progression was statistically significant (p < 0.001, Pearson's chi-square test). No statistically significant association was observed between the patterns and disease severity (p = 0.055, Fisher's exact test). No fibrous changes in the lesions were observed on follow-up CT scans. Conclusion The serial CT scans of COVID-19 patients showed the spectrum of COVID pneumonia CT manifestations as different phases of lung injury and repair.

• Journal of the Korean Society of Radiology
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• v.12 no.3
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• pp.403-407
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• 2018

Currently, the highest cancer death rate in Korea is lung cancer, which is a typical cancer that is difficult to detect early. Low-dose chest CT is being used for early detection, which has a greater lung cancer diagnosis rate of about three times than regular chest x-ray images. However, low-dose chest CT not only significantly reduces image resolution but also has a weak signal and is sensitive to noise. Also, air filled lungs are low-density organs and the presence of noise can significantly affect early diagnosis of cancer. This study used Visual C++ to set a circle inside a large circle with a density of 2.0, with a density of 1.0, which is the density of water, in which five small circle of mathematics have different densities. Gaussian noise was generated by 1%, 2%, 3%, and 4% respectively to determine the effect of noise on the mean value, the standard deviation value, and the relative noise ratio(SNR). In areas where the density difference between the large and small circles was greatest in the event of 1 % noise, the SNR in the area with the greatest variation in noise was 4.669, and in areas with the lowest density difference, the SNR was 1.183. In addition, the SNR values can be seen to be high if the same results are obtained for both positive and negative densities. Quality was also clearly visible when the density difference was large, and if the noise level was increased, the SNR was reduced to significantly affect the noise. Low-density organs or organs in areas of similar density to cancers, will have significant noise effects, and the effects of density differences on the probability of noise will affect diagnosis.