Coronary artery disease (CAD) narrows vessel lumens at the sites of atherosclerosis, increasing the risk of myocardial ischemia or infarction. Early and accurate diagnosis of CAD is crucial to significantly improve prognosis and management. CT angiography (CTA) is a noninvasive imaging technique that enables assessment of vascular structure and stenosis with high resolution and contrast. Coronary CTA is useful in the diagnosis of CAD. Recently, the CAD-reporting and data system (CAD-RADS), a diagnostic classification system based on coronary CTA, has been developed to improve intervention efficacy in patients suspected of CAD. While the CADRAD is based on CTA, it includes borderline categories where interpreting the coronary artery status solely based on CTA findings may be challenging. This review introduces CTA findings that fall within the CAD-RADS categories that necessitate additional tests to decide to perform invasive coronary angiography and discusses appropriate management strategies.

Coronary artery disease (CAD) narrows vessel lumens at the sites of atherosclerosis, increasing the risk of myocardial ischemia or infarction. Early and accurate diagnosis of CAD is crucial to significantly improve prognosis and management. CT angiography (CTA) is a noninvasive imaging technique that enables assessment of vascular structure and stenosis with high resolution and contrast. Coronary CTA is useful in the diagnosis of CAD. Recently, the CAD-reporting and data system (CAD-RADS), a diagnostic classification system based on coronary CTA, has been developed to improve intervention efficacy in patients suspected of CAD. While the CADRAD is based on CTA, it includes borderline categories where interpreting the coronary artery status solely based on CTA findings may be challenging. This review introduces CTA findings that fall within the CAD-RADS categories that necessitate additional tests to decide to perform invasive coronary angiography and discusses appropriate management strategies.

Coronary artery disease (CAD) narrows vessel lumens at the sites of atherosclerosis, increasing the risk of myocardial ischemia or infarction. Early and accurate diagnosis of CAD is crucial to significantly improve prognosis and management. CT angiography (CTA) is a noninvasive imaging technique that enables assessment of vascular structure and stenosis with high resolution and contrast. Coronary CTA is useful in the diagnosis of CAD. Recently, the CAD-reporting and data system (CAD-RADS), a diagnostic classification system based on coronary CTA, has been developed to improve intervention efficacy in patients suspected of CAD. While the CADRAD is based on CTA, it includes borderline categories where interpreting the coronary artery status solely based on CTA findings may be challenging. This review introduces CTA findings that fall within the CAD-RADS categories that necessitate additional tests to decide to perform invasive coronary angiography and discusses appropriate management strategies.

Background: We evaluated the radiologic, pulmonary functional, and antibody statuses of coronavirus disease 2019 (COVID-19) patients 6 and 18 months after discharge, comparing changes in status and focusing on risk factors for residual computed tomography (CT) abnormalities. Methods: This prospective cohort study was conducted on COVID-19 patients discharged between April 2020 and January 2021. Chest CT, pulmonary function testing (PFT), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) measurements were performed 6 and 18 months after discharge. We evaluated factors associated with residual CT abnormalities and the correlation between lesionvolume in CT (lesionvolume ), PFT, and IgG levels. Results: This study included 68 and 42 participants evaluated 6 and 18 months, respectively, after hospitalizations for COVID-19. CT abnormalities were noted in 22 participants (32.4%) at 6 months and 13 participants (31.0%) at 18 months. Lesionvolume was significantly lower at 18 months than 6 months (P < 0.001). Patients with CT abnormalities at 6 months showed lower forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC), and patients with CT abnormalities at 18 months exhibited lower FVC. FVC significantly improved between 6 and 18 months of follow-up (all P < 0.0001). SARS-CoV-2 IgG levels were significantly higher in patients with CT abnormalities at 6 and 18 months (P < 0.001). At 18-month follow-up assessments, age was associated with CT abnormalities (odds ratio, 1.17; 95% confidence interval, 1.03–1.32; P = 0.01), and lesionvolume showed a positive correlation with IgG level (r = 0.643, P < 0.001). Conclusion At 18-month follow-up assessments, 31.0% of participants exhibited residual CT abnormalities. Age and higher SARS-CoV-2 IgG levels were significant predictors, and FVC was related to abnormal CT findings at 18 months. Lesionvolume and FVC improved between 6 and 18 months.

Background: To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). Methods: This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). Results: In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. Conclusion Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.

Purpose: The aim of this study is to demonstrate the association between recurrent atrial fibrillation (AF) and left ventricular (LV) adverse remodeling after catheter ablation and to evaluate the change of myocardial extracellular volume fraction (ECV) by catheter ablation outcomes. Materials and Methods: We retrospectively recruited 60 patients (44 men and 16 women) with a median age of 57 years (range, 32–78 years) who underwent cardiac MRI before and at 6–12 months after catheter ablation of AF. Cardiac MRI quantified myocardial ECV (%) in the left ventricle. Depending on myocardial ECV after catheter ablation, patients were divided into two groups: 1) LV adverse remodeling with ECV ≥ 28%; and 2) no adverse LV remodeling with ECV < 28%. Multivariable analysis was performed to assess the association between recurrent AF and LV remodeling. Results: Of 60 patients, 21 (35%) were in the LV adverse remodeling group (mean ECV ± standard deviation [SD]: 29.8% ± 1.4%) and 39 (65%) were in the no adverse LV remodeling group (mean ECV ± SD: 24.7% ± 1.5%). The incidence of recurrent AF was significantly greater in the LV adverse remodeling group than in the no adverse LV remodeling group (81% vs. 13%, p < 0.001). In patients with recurrent AF, mean myocardial ECV significantly increased from 27.7% ± 2.3% to 29.2% ± 2.3% (p = 0.004) after catheter ablation. In a multivariable analysis after adjusting sex, age, and myocardial ECV before catheter ablation, recurrent AF was independently associated with LV adverse remodeling after catheter ablation (odds ratio: 28.9, 95% confidence interval: 6.8–121.7, p < 0.001). Conclusion When monitoring with cardiac MRI, sustained AF was significantly associated with LV adverse remodeling through an increase in myocardial ECV after catheter ablation of AF.

Background: To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). Methods: An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. Results: The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (−14.90–27.61), 6.21% (−9.62–22.03), and 2.68% (−8.57–13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. Conclusion Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.

Hypersensitivity pneumonitis (HP) is an interstitial lung disease (ILD) characterized by an inhaled inciting antigen that leads to the inflammation of the lung parenchyma and small airway with immunologic reactions. Over the last decades, the most effective therapeutic option for HP has been limited to antigen avoidance. The differential diagnosis of HP from other ILDs is the beginning of treatment as well as diagnosis. However, the presence of several overlapping clinical and radiologic features makes differentiating HP from other ILDs particularly challenging. In 2020, a multidisciplinary committee of experts from the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax suggested a new clinical practice guideline classifying HP into nonfibrotic and fibrotic phenotypes on the basis of chest high-resolution CT (HRCT) findings. Therefore, we introduced a new diagnostic algorithm based on chest HRCT in the clinical practice guideline for the diagnosis of HP.

Objective: To assess the effect of left atrial appendage (LAA) isolation on LAA emptying and left atrial (LA) function using cardiac MRI in patients who underwent successful catheter ablation of atrial fibrillation (AF). Materials and Methods: This retrospective study included 84 patients (mean age, 59 ± 10 years; 67 males) who underwent cardiac MRI after successful catheter ablation of AF. According to the electrical activity of LAA after catheter ablation, patients showed either LAA isolation or LAA normal activity. The LAA emptying phase (LAA-EP, in the systolic phase [SP] or diastolic phase), LAA emptying flux (LAA-EF, mL/s), and LA ejection fraction (LAEF, %) were evaluated by cardiac MRI. Results: Of the 84 patients, 61 (73%) and 23 (27%) patients showed LAA normal activity and LAA isolation, respectively.Incidence of LAA emptying in SP was significantly higher in LAA isolation (91% vs. 0%, p < 0.001) than in LAA normal activation. LAA-EF was significantly lower in LAA isolation (40.1 ± 16.2 mL/s vs. 80.2 ± 25.1 mL/s, pp < 0.001) than in LAA normal activity. Furthermore, LAEF was significantly lower in LAA isolation (23.7% ± 11.2% vs. 31.1% ± 16.6%, p = 0.04) than in LAA normal activity. Multivariate analysis demonstrated that the LAA-EP was independent from LAEF (p = 0.01). Conclusion LAA emptying in SP may be a critical characteristic of LAA isolation, and it may adversely affect the LAEF after catheter ablation of AF.

Objective: To assess the effect of left atrial appendage (LAA) isolation on LAA emptying and left atrial (LA) function using cardiac MRI in patients who underwent successful catheter ablation of atrial fibrillation (AF). Materials and Methods: This retrospective study included 84 patients (mean age, 59 ± 10 years; 67 males) who underwent cardiac MRI after successful catheter ablation of AF. According to the electrical activity of LAA after catheter ablation, patients showed either LAA isolation or LAA normal activity. The LAA emptying phase (LAA-EP, in the systolic phase [SP] or diastolic phase), LAA emptying flux (LAA-EF, mL/s), and LA ejection fraction (LAEF, %) were evaluated by cardiac MRI. Results: Of the 84 patients, 61 (73%) and 23 (27%) patients showed LAA normal activity and LAA isolation, respectively.Incidence of LAA emptying in SP was significantly higher in LAA isolation (91% vs. 0%, p < 0.001) than in LAA normal activation. LAA-EF was significantly lower in LAA isolation (40.1 ± 16.2 mL/s vs. 80.2 ± 25.1 mL/s, pp < 0.001) than in LAA normal activity. Furthermore, LAEF was significantly lower in LAA isolation (23.7% ± 11.2% vs. 31.1% ± 16.6%, p = 0.04) than in LAA normal activity. Multivariate analysis demonstrated that the LAA-EP was independent from LAEF (p = 0.01). Conclusion LAA emptying in SP may be a critical characteristic of LAA isolation, and it may adversely affect the LAEF after catheter ablation of AF.