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Background: Compared to normal high-density lipoprotein (HDL) cholesterol values, very high HDL cholesterol is associated with a higher incidence of mortality and atherosclerotic cardiovascular disease (ASCVD). As such, clinical risk stratification among persons with very high HDL cholesterol is challenging. Objectives: Among persons with very high HDL cholesterol, the purpose was to determine the prevalence of coronary artery calcium (CAC) and compare the association between traditional risk factors vs CAC for all-cause mortality and ASCVD. Methods: The primary analysis was completed among 446 participants from the Cedars-Sinai Medical Center of the CAC Consortium with very high HDL cholesterol (≥77 mg/dL in men, ≥97 mg/dL in women). Cox proportional hazards regression assessed the association of CAC and traditional risk factors with all-cause mortality during a median follow-up of 10.7 years. Replication and validation analyses were performed for all-cause mortality among 119 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) with very high HDL cholesterol, who also had information on incident ASCVD. Results: The mean age was 57.9 years old, 49% were women, and the median HDL cholesterol was 98 mg/dL. One-half of participants (50%) had prevalent CAC, in whom the median CAC score was 118. Prevalent CAC conferred a 3.6-fold higher risk of all-cause mortality (HR: 3.64; 95% CI: 1.21-11.01), which appeared to be a more robust predictor than individual traditional risk factors beyond age. In the validation sample, prevalent CAC but not individual traditional risk factors were associated with all-cause mortality (HR: 2.39; 95% CI: 1.07-5.34) and a 4.0-fold higher risk of ASCVD (HR: 4.06; 95% CI: 1.11-14.84). Conclusions: Measurement of CAC may facilitate clinical risk assessment among individuals with very high HDL cholesterol.

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Background and aims: Cardiac ParaGangliomas (PGLs) are rare extra-adrenal tumours that arise from chromaffin cells of the sympathetic ganglia. PGL are often diagnosed incidentally, with no symptoms or symptoms related to cardiovascular dysfunction. Methods: Cardiac Computed Tomography (CCT) and Cardiac Magnetic Resonance (CMR) can detect the correct morphology and position of the lesion and provide proper tissue characterization.Nuclear medicine imaging, with Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) with specific radiotracers, can evaluate the functionality of the PGL and to distinguish a secreting from a non-secreting tumour. Results: In association with biochemical parameters, a multimodal imaging approach, not yet standardized, can be useful both in the diagnosis, in the monitoring and in the treatment planning. Conclusions: In this systematic review, we aim to investigate the role of diagnostic imaging, in particular CCT, CMR, PET and SPECT in diagnosis, characterization and monitoring of cardiac PGLs.

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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 lesion volume 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. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0008573.

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In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR.

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RATIONALE AND OBJECTIVES: Evidence is building in support of the clinical utility of atherosclerotic plaque imaging by computed tomography angiography (CTA). There is increasing organized activity to embrace non-calcified plaque (NCP) as a formally defined biomarker for clinical trials, and high-risk plaque (HRP) for clinical care, as the most relevant measures for the field to advance and worthy of community efforts to validate. Yet the ability to assess the quantitative performance of any given specific solution to make these measurements or classifications is not available. Vendors use differing definitions, assessment metrics, and validation data sets to describe their offerings without clinician users having the capability to make objective assessments of accuracy and precision and how this affects diagnostic confidence. MATERIALS AND METHODS: The QIBA Profile for Atherosclerosis Biomarkers by CTA was created by the Quantitative Imaging Biomarkers Alliance (QIBA) to improve objectivity and decrease the variability of noninvasive plaque phenotyping. The Profile provides claims on the accuracy and precision of plaque measures individually and when combined. RESULTS: Individual plaque morphology measurements are evaluated in terms of bias (accuracy), slope (consistency of the bias across the measurement range, needed for measurements of change), and variability. The multiparametric plaque stability phenotype is evaluated in terms of agreement with expert pathologists. The Profile is intended for a broad audience, including those engaged in discovery science, clinical trials, and patient care. CONCLUSION: This report provides a rationale and overview of the Profile claims and how to comply with the Profile in research and clinical practice. SUMMARY STATEMENT: This article summarizes objective means to validate the analytical performance of non-calcified plaque (NCP), other emerging plaque morphology measurements, and multiparametric histology-defined high-risk plaque (HRP), as outlined in the QIBA Profile for Atherosclerosis Biomarkers by CTA.

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PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. MATERIALS AND METHODS: In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). RESULTS: Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 (P < 0.001). CONCLUSIONS: The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.

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This document summarizes the relevant literature for the selection of preprocedural imaging in three clinical scenarios in patients needing endovascular treatment or cardioversion of atrial fibrillation. These clinical scenarios include preprocedural imaging prior to radiofrequency ablation; prior to left atrial appendage occlusion; and prior to cardioversion. The appropriateness of imaging modalities as they apply to each clinical scenario is rated as usually appropriate, may be appropriate, and usually not appropriate to assist the selection of the most appropriate imaging modality in the corresponding clinical scenarios. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

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BACKGROUND: Patients with myocardial ischemia without obstructive coronary artery disease often have coronary microvascular dysfunction (CMD) and associated increased risk of cardiovascular (CV) events and anginal hospitalizations. Epicardial adipose tissue (EAT) covers much of the myocardium and coronary arteries and when dysfunctional, secretes proinflammatory cytokines and is associated with CV events. While oxidative stress and systemic inflammation are associated with CMD, the relationship between EAT and CMD in women is not well known. METHODS: Women diagnosed with CMD (n = 21) who underwent coronary computed tomography with coronary artery calcium (CAC) scoring were compared to a reference group (RG) of women referred for CAC screening for preventive risk assessment (n = 181). EAT attenuation (Hounsfield units (HU)) was measured adjacent to the proximal right coronary artery, along with subcutaneous adipose tissue (SCAT). Two-sample t-tests with unequal variances were utilized. RESULTS: Mean age of the CMD group was 56 ± 8 years and body mass index (BMI) was 31.6 ± 6.8 kg/m2. CV risk factors in the CMD group were prevalent: 67 % hypertension, 44 % hyperlipidemia, and 33 % diabetes. Both CMD and RG had similar CAC score (25.86 ± 59.54 vs. 24.17 ± 104.6; p = 0.21. In the CMD group, 67 % had a CAC of 0. Minimal atherosclerosis (CAD-RADS 1) was present in 76 % of women with CMD. The CMD group had lower EAT attenuation than RG (-103.3 ± 6.33 HU vs. -97.9 ± 8.3 HU, p = 0.009, respectively). There were no differences in SCAT attenuation. Hypertension, smoking history, age, BMI, and CAC score did not correlate with EAT in either of the groups. CONCLUSIONS: Women with CMD have decreased EAT attenuation compared to RG women. EAT-mediated inflammation and changes in vascular tone may be a mechanistic contributor to abnormal microvascular reactivity. Clinical trials testing therapeutic strategies to decrease EAT may be warranted in the management of CMD.

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This document summarizes the relevant literature for the selection of preprocedural imaging in three clinical scenarios in patients needing endovascular treatment or cardioversion of atrial fibrillation. These clinical scenarios include preprocedural imaging prior to radiofrequency ablation; prior to left atrial appendage occlusion; and prior to cardioversion. The appropriateness of imaging modalities as they apply to each clinical scenario is rated as usually appropriate, may be appropriate, and usually not appropriate to assist the selection of the most appropriate imaging modality in the corresponding clinical scenarios. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

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Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.

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PURPOSE: Coronary artery plaque burden, low attenuation non-calcified plaque (LAP), and pericoronary adipose tissue (PCAT) on coronary CT angiography (CCTA), have been linked to future cardiac events. The purpose of this study was to evaluate intra- and inter reader reproducibility in the quantification of coronary plaque burden and its characteristics using an artificial intelligence-enhanced semi-automated software. MATERIALS AND METHODS: A total of 10 women and 6 men, aged 52 (IQR 49-58) underwent CCTA using a Siemens Somatom Force, Somatom Definition AS and Somatom Definition Flash scanners. Two expert readers utilized dedicated semi-automatic software (vascuCAP, Elucid Bioimaging, Wenham, MA) to assess calcified plaque, low attenuation plaque and PCAT. Readers were blinded to all clinical information and repeated their analysis at 6 weeks in random order to minimize recall bias. Data analysis was performed on the right and left coronary arteries. Intra- and inter-reader reproducibility was compared using Pearson correlation coefficient, while absolute values between analyses and readers were compared with paired non-parametric tests. This is a sub-study of the Specialized Center of Research Excellence (SCORE) clinical trial (5U54AG062334). RESULTS: A total of 64 vessels from 16 patients were analyzed. Intra-reader Pearson correlation coefficients for calcified plaque volume, LAP volume and PCAT volumes were 0.96, 0.99 and 0.92 for reader 1 and 0.94, 0.94 and 0.95 for reader 2, respectively, (all p < 0.0001). Inter-reader Pearson correlation coefficients for calcified plaque volume, LAP and PCAT volumes were 0.92, 0.96 and 0.78, and 0.99, 0.99 and 0.93 on the second analyses, all had a p value <0.0001. There was no significant bias on the corresponding Bland-Altman analyses. CONCLUSION: Volume measurement of coronary plaque burden and PCAT volume can be performed with high intra- and inter-reader agreement.

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Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.

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BACKGROUND: Although a coronary artery calcium (CAC) of ≥1,000 is a subclinical atherosclerosis threshold to consider combination lipid-lowering therapy, differentiating very high from high atherosclerotic cardiovascular disease (ASCVD) risk in this patient population is not well-defined. OBJECTIVES: Among persons with a CAC of ≥1,000, the authors sought to identify risk factors equating with very high-risk ASCVD mortality rates. METHODS: The authors studied 2,246 asymptomatic patients with a CAC of ≥1,000 from the CAC Consortium without a prior ASCVD event. Cox proportional hazards regression modelling was performed for ASCVD mortality during a median follow-up of 11.3 years. Crude ASCVD mortality rates were compared with those reported for secondary prevention trial patients classified as very high risk, defined by ≥2 major ASCVD events or 1 major event and ≥2 high-risk conditions (1.4 per 100 person-years). RESULTS: The mean age was 66.6 years, 14% were female, and 10% were non-White. The median CAC score was 1,592 and 6% had severe left main (LM) CAC (vessel-specific CAC ≥300). Diabetes (HR: 2.04 [95% CI: 1.47-2.83]) and severe LM CAC (HR: 2.32 [95% CI: 1.51-3.55]) were associated with ASCVD mortality. The ASCVD mortality per 100 person-years for all patients was 0.8 (95% CI: 0.7-0.9), although higher rates were observed for diabetes (1.4 [95% CI: 0.8-1.9]), severe LM CAC (1.3 [95% CI: 0.6-2.0]), and both diabetes and severe LM CAC (7.1 [95% CI: 3.4-10.8]). CONCLUSIONS: Among asymptomatic patients with a CAC of ≥1,000 without a prior index event, diabetes, and severe LM CAC define very high risk ASCVD, identifying individuals who may benefit from more intensive prevention therapies across several domains, including low-density lipoprotein-cholesterol lowering.

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In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.

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BACKGROUND: The development of thoracic aortic calcium (TAC) temporally precedes coronary artery calcium more often in women versus men. Whether TAC density and area confer sex-specific differences in atherosclerotic cardiovascular disease (ASCVD) risk is unknown. METHODS: We studied 5317 primary prevention patients who underwent coronary artery calcium scoring on noncontrast cardiac gated computed tomography with TAC >0. The Agatston TAC score (Agatston units), density (Hounsfield units), and area (mm2) were compared between men and women. Cox proportional hazards regression calculated adjusted hazard ratios for TAC density-area groups with ASCVD mortality, adjusting for traditional risk factors, coronary artery calcium, and TAC. Multinomial logistic regression calculated adjusted odds ratios for the association between traditional risk factors and TAC density-area groups. RESULTS: The mean age was 60.7 years, 38% were women, and 163 ASCVD deaths occurred over a median of 11.7-year follow-up. Women had higher median TAC scores (97 versus 84 Agatston units; P=0.004), density (223 versus 210 Hounsfield units; P<0.001), and area (37 versus 32 mm2; P=0.006) compared with men. There was a stepwise higher incidence of ASCVD deaths across increasing TAC density-area groups in men though women with low TAC density relative to TAC area (3.6 per 1000 person-years) had survival probability commensurate with the high-density-high-area group (4.8 per 1000 person-years). Compared with low TAC density-area, low TAC density/high TAC area conferred a 3.75-fold higher risk of ASCVD mortality in women (adjusted hazard ratio, 3.75 [95% CI, 1.13-12.44]) but not in men (adjusted hazard ratio, 1.16 [95% CI, 0.48-2.84]). Risk factors most strongly associated with low TAC density/high TAC area differed in women (diabetes: adjusted odds ratio, 2.61 [95% CI, 1.34-5.07]) versus men (hypertension: adjusted odds ratio, 1.45 [95% CI, 1.11-1.90]). CONCLUSIONS: TAC density-area phenotypes do not consistently associate with ASCVD mortality though low TAC density relative to area may be a marker of increased ASCVD risk in women.

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INTRODUCTION: Although pericoronary adipose tissue (PCAT) is a component of the epicardial adipose tissue (EAT) depot, they may have different associations to coronary artery disease (CAD). We explored relationships between pericoronary adipose tissue mean attenuation (PCATMA) and EAT measurements in coronary CT angiography (CCTA) in patients with and without CAD. MATERIAL AND METHODS: CCTA scans of 185 non-CAD and 81 CAD patients (86.4% >50% stenosis) were included and retrospectively analyzed. PCATMA and EAT density/volume were measured and analyzed by sex, including associations with age, risk factors and tube voltage using linear regression models. RESULTS: In non-CAD and CAD, mean PCATMA and EAT volume were higher in men than in women (non-CAD: -92.5 ± 10.6HU vs -96.2 ± 8.4HU, and 174.4 ± 69.1 cm3 vs 124.1 ± 57.3 cm3; CAD: -92.2 ± 9.0HU vs -97.4 ± 9.7HU, and 193.6 ± 62.5 cm3 vs 148.5 ± 50.5 cm3 (p < 0.05)). EAT density was slightly lower in men than women in non-CAD (-96.4 ± 6.3HU vs -94.4 ± 5.5HU (p < 0.05)), and similar in CAD (-98.2 ± 5.2HU vs 98.2 ± 6.4HU). There was strong correlation between PCATMA and EAT density (non-CAD: r = 0.725, p < 0.001, CAD: r = 0.686, p < 0.001) but no correlation between PCATMA and EAT volume (non-CAD: r = 0.018, p = 0.81, CAD: r = -0.055, p = 0.63). A weak inverse association was found between EAT density and EAT volume (non-CAD: r = -0.244, p < 0.001, CAD: r = -0.263, p = 0.02). In linear regression models, EAT density was significantly associated with PCATMA in both non-CAD and CAD patients independent of risk factors and tube voltage. CONCLUSION: In CAD and non-CAD patients, EAT density, but not EAT volume, showed significant associations with PCATMA. Compared to women, men had higher PCATMA and EAT volume independently of disease status, but similar or slightly lower EAT density. Differences in trends and relations of PCATMA and EAT by sex could indicate that personalized interpretation and thresholding is needed.

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Coronary artery calcium (CAC) is the measure of subclinical coronary artery atherosclerosis most strongly associated with atherosclerotic cardiovascular disease (ASCVD) risk. However, CAC is rarely reported in the inpatient setting to guide chest pain management. We present a case of very high CAC in a 64-year-old woman with hypertension, type 2 diabetes, and hyperlipidemia presenting with dyspnea. Initial electrocardiogram (ECG) demonstrated normal conduction with a heart rate of 76 beats/min, but new T-wave inversions in V1-V4 and a high-sensitivity troponin-I (hsTnI) value of 6 ng/L (normal < 6 ng/L). Repeat ECG in the emergency department showed normal sinus rhythm (heart rate of 80 beats/min); however, it subsequently demonstrated a left bundle branch block (LBBB) with a repeat hsTnI of 7 ng/L. Stress testing with pharmacologic single-photon emission computerized tomography did not show scintigraphic evidence of ischemia but noted extensive CAC and a concern for balanced ischemia. Subsequent coronary computed tomography angiography (CCTA) showed nonobstructive disease and a total Agatston CAC score of 1262. Invasive evaluation with left heart catheterization was deferred given the patient's unchanged symptoms and CCTA findings. Statin therapy was intensified and aspirin, metoprolol succinate, and antihypertension therapies were continued. Initiation of glucose-lowering therapy and lipoprotein(a) testing was strongly recommended on follow-up. Our case suggests that CAC ⩾ 1000 may be incidentally associated with transient LBBB during the workup of coronary artery disease. Here, we specifically show that functional testing that incorporates measurement of CAC burden can help to improve ASCVD-preventive pharmacotherapy initiation and intensification beyond the identification of obstructive disease alone.

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Dual-energy computed tomography (DECT) acquires images using two energy spectra and offers a variation of reconstruction techniques for improved cardiac imaging. Virtual monoenergetic images decrease artifacts improving coronary plaque and stent visualization. Further, contrast attenuation is increased allowing significant reduction of contrast dose. Virtual non-contrast reconstructions enable coronary artery calcium scoring from contrast-enhanced scans. DECT provides advanced plaque imaging with detailed analysis of plaque components, indicating plaque stability. Extracellular volume assessment using DECT offers noninvasive detection of myocardial fibrosis. This review aims to outline the current cardiac applications of DECT, summarize recent literature, and discuss their findings.

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