AbbreviationsCOVID-19 (coronavirus disease 2019), CT (computed tomography), CXR (chest radiograph), RT-PCR (reverse transcriptase-polymerase chain reaction), ICU (intensive care unit), WBC (white blood cell), LDH (lactate dehydrogenase), CRP (C-reactive protein) SummaryVaccinated patients with COVID-19 breakthrough infections showed fewer chest CT findings of pneumonia compared to unvaccinated patients. Key Results1. Of 761 hospitalized patients with COVID-19 disease and chest radiographs, 77% (587/761) were in unvaccinated patients while breakthrough infection in fully vaccinated subjects occurred in 6.2% (47/761) patients.2. The initial chest x-ray showed no pneumonia in 75% of fully vaccinated patients with breakthrough infection and 63% of unvaccinated patients (p=.37).3. In 412 patients with chest CT during hospitalization, a CT showed no pneumonia in 59% of fully vaccinated patients with breakthrough infection and 27% of unvaccinated patients (p=.01).

ObjectiveTo compare the observer preference of image quality and radiation dose between non-grid, grid-like, and grid images.Materials and MethodsEach of the 38 patients underwent bedside chest radiography with and without a grid. A grid-like image was generated from a non-grid image using SimGrid software (Samsung Electronics Co. Ltd.) employing deep-learning-based scatter correction technology. Two readers recorded the preference for 10 anatomic landmarks and the overall appearance on a five-point scale for a pair of non-grid and grid-like images, and a pair of grid-like and grid images, respectively, which were randomly presented. The dose area product (DAP) was also recorded. Wilcoxon's rank sum test was used to assess the significance of preference.ResultsBoth readers preferred grid-like images to non-grid images significantly (p < 0.001); with a significant difference in terms of the preference for grid images to grid-like images (p = 0.317, 0.034, respectively). In terms of anatomic landmarks, both readers preferred grid-like images to non-grid images (p < 0.05). No significant differences existed between grid-like and grid images except for the preference for grid images in proximal airways by two readers, and in retrocardiac lung and thoracic spine by one reader. The median DAP were 1.48 (range, 1.37–2.17) dGy*cm2 in grid images and 1.22 (range, 1.11–1.78) dGy*cm2 in grid-like images with a significant difference (p < 0.001).ConclusionThe SimGrid software significantly improved the image quality of non-grid images to a level comparable to that of grid images with a relatively lower level of radiation exposure.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

• Cone-beam CT-guided repeat biopsy yielded high diagnostic specimen acquisition rate. • Biopsy-related features were associated with the detection of T790M mutation. • Target tumour size was an independent predictor of the T790M detection. • Biopsy targeting metastatic lung nodules might help detect the T790M mutation.

Dual-energy CT systems and fluid characteristics did not have a significant effect on the IoD measurement accuracy. A cutoff of IoD for the determination of a truly enhancing lesion on DECT would be 0.4 mg/mL.

Percutaneous transthoracic needle biopsy (PTNB) is one of the essential diagnostic procedures for pulmonary lesions. Its role is increasing in the era of CT screening for lung cancer and precision medicine. The Korean Society of Thoracic Radiology developed the first evidence-based clinical guideline for PTNB in Korea by adapting pre-existing guidelines. The guideline provides 39 recommendations for the following four main domains of 12 key questions: the indications for PTNB, pre-procedural evaluation, procedural technique of PTNB and its accuracy, and management of post-biopsy complications. We hope that these recommendations can improve the diagnostic accuracy and safety of PTNB in clinical practice and promote standardization of the procedure nationwide.

The purpose of this study was to investigate regional air volume changes at the acinar scale of the lung in chronic obstructive pulmonary disease (COPD) patients using an image registration technique. Materials and Methods: Thirty-four emphysema patients and 24 subjects with normal chest CT and pulmonary function test (PFT) results were included in this retrospective study for which informed consent was waived by the institutional review board. After lung segmentation, a mass-preserving image registration technique was used to compute relative regional air volume changes (RRAVCs) between inspiration and expiration CT scans. After determining the appropriate thresholds of RRAVCs for low ventilation areas (LVAs), they were displayed and analyzed using color maps on the background inspiration CT image, and compared with the low attenuation area (LAA) map. Correlations between quantitative CT parameters and PFTs were assessed using Pearson's correlation test, and parameters were compared between emphysema and normal-CT patients using the Student's t-test. Results: LVA percentage with an RRAVC threshold of 0.5 (%LVA 0.5) showed the strongest correlations with FEV 1 /FVC (r = À0.566), FEV 1 (r = À0.534), %LAA-950insp (r = 0.712), and %LAA-856exp (r = 0.775). %LVA 0.5 was significantly higher (P < 0.001) in COPD patients than normal subjects. Despite the identical appearance of emphysematous lesions on the LAA-950insp map, the RRAVC map depicted a wide range of ventilation differences between these LAA clusters. Conclusion: RRAVC-based %LVA 0.5 correlated well with FEV 1 /FVC, FEV 1 , %LAA-950insp and %LAA-856exp. RRAVC holds the potential for providing additional acinar scale functional information for emphysematous LAAs in inspiratory CT images, providing the basis for a novel set for emphysematous phenotypes.

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