Vaccinated patients with COVID-19 breakthrough infections showed fewer chest CT findings of pneumonia compared to unvaccinated patients. Background Since vaccines against coronavirus disease 2019 (COVID-19) became available, rare breakthrough infections have been reported despite their high efficacies. Purpose To evaluate the clinic...

Periods of SARS-CoV-2 Omicron versus the Delta variant prevalence and vaccination status were associated with better clinical outcomes and less severe pneumonia at CT in hospitalized patients with ... Background Few reports have evaluated the effect of the SARS-CoV-2 variant and vaccination on the clinical and imaging features of COVID-19. Purpose To evaluate and compare the effect of vaccinatio...

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.

Lee J, et al. Korean J Radiol. 2023 Apr;24:e32. https://doi.org/10.3348/kjr.2022.0898

Hwang MH, et al. Korean J Radiol. 2024 Sep;25(9):833-842. https://doi.org/10.3348/kjr.2024.0472

Background and Objectives: Glomus tumors are rare benign tumors. The majority of them affect the skin the most and are rarer in the trachea, where the glomus bodies may not be present. Only scarce reports of tracheal glomus tumors have been reported solely with case reports of relevant articles. Materials and Methods: A 53-year-old man, with a free previous medial history, presented to our hospital with tracheal mass which was incidentally found. He did not complain of any specific symptoms associated with the tracheal tumor. The contrast-enhanced chest computed tomography (CT) revealed an avid enhancing nodular lesion, which is similar to blood vessels, in the trachea, 3 cm above the carina level without definite airway obstruction. Results: Successful tracheal resection and end-to-end anastomosis were performed on the patients; therefore, the final post-operative pathologic findings revealed a benign tracheal glomus tumor. The follow-up CT scan four months after operation showed complete removal of the tumor. Conclusion: Tracheal glomus tumors, even rare entities, could be considered as a differential diagnosis if a highly enhancing mass appears on CT images.