Infarcted regions of myocardium exhibit functional impairment ranging in severity from hypokinesis to dyskinesis. We sought to quantify the effects of injecting a calcium hydroxyapatite-based tissue filler on the passive material response of infarcted left ventricles. Three-dimensional (3D) finite element models of the left ventricle were developed using 3D echocardiography data from sheep with a treated and untreated anteroapical infarct, in order to estimate the material properties (stiffness) in the infarct and remote regions. This was accomplished by matching experimentally determined left ventricular volumes, and minimizing radial strain in the treated infarct, which is indicative of akinesia. The nonlinear stress-strain relationship for the diastolic myocardium was anisotropic with respect to the local muscle fiber direction, and an elastance model for active fiber stress was incorporated. It was found that the passive stiffness parameter, C, in the treated infarct region is increased by nearly 345 times the healthy remote value. Additionally, the average myofiber stress in the treated left ventricle was significantly reduced in both the remote and infarct regions. Overall, injection of tissue filler into the infarct was found to render it akinetic and reduce stress in the left ventricle, which could limit the adverse remodeling that leads to heart failure.

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.

Rapid CT-based tissue characterization is feasible in patients referred for TAVR. Decreased PM area and increased SAT density are associated with long-term mortality after TAVR, even after accounting for age, sex, BMI, and STS score. Further studies are necessary to interrogate sex-specific relationships between CT tissue metrics and mortality and whether CT measures are incremental to well-established frailty metrics.

Wall shear stress (WSS) has been shown to be associated with myocardial infarction (MI) and progression of atherosclerosis. Wall elasticity is an important feature of hemodynamic modeling affecting WSS calculations. The objective of this study was to investigate the role of wall elasticity on WSS, and justify use of either rigid or elastic models in future studies. Digital anatomic models of the aorta and coronaries were created based on coronary computed tomography angiography (CCTA) in four patients. Hemodynamics was computed in rigid and elastic models using a finite element flow solver. WSS in five timepoints in the cardiac cycle and time averaged wall shear stress (TAWSS) were compared between the models at each 3 mm subsegment and 4 arcs in cross sections along the centerlines of coronaries. In the left main (LM), proximal left anterior descending (LAD), left circumflex (LCX), and proximal right coronary artery (RCA) of the elastic model, the mean percent radial increase 5.95 ± 1.25, 4.02 ± 0.97, 4.08 ± 0.94, and 4.84 ± 1.05%, respectively. WSS at each timepoint in the cardiac cycle had slightly different values; however, when averaged over the cardiac cycle, there were negligible differences between the models. In both the subsegments (n = 704) and subarc analysis, TAWSS in the two models were highly correlated (r = 0.99). In investigation on the effect of coronary wall elasticity on WSS in CCTA-based models, the results of this study show no significant differences in TAWSS justifying using rigid wall models for future larger studies.

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Background Rapid improvement of scanner and postprocessing technology as well as the introduction of minimally invasive procedures requiring preoperative imaging have led to the broad utilization of cardiac computed tomography (CT) beyond coronary CT angiography (CTA). Method This review article presents an overview of recent literature on cardiac CT. The goal is to summarize the current guidelines on performing cardiac CT and to list established as well as emerging techniques with a special focus on extracoronary applications. Results and Conclusion Most recent guidelines for the appropriate use of cardiac CT include the evaluation of coronary artery disease, cardiac morphology, intra- and extracardiac structures, and functional and structural assessment of the myocardium under certain conditions. Besides coronary CTA, novel applications such as the calculation of a CT-derived fractional flow reserve (CT-FFR), assessment of myocardial function and perfusion imaging, as well as pre-interventional planning in valvular heart disease or prior pulmonary vein ablation in atrial fibrillation are becoming increasingly important. Especially these extracoronary applications are of growing interest in the field of cardiac CT and are expected to be gradually implemented in the daily clinical routine. Key Points:  Citation Format

C oronary artery calcium (CAC), as assessed by cardiac CT, is a well-established and robust risk predictor for major adverse cardiovascular events, independent of traditional cardiovascular risk factors (1-3). Agatston score (AS) is the most commonly used method to quantify CAC in clinical practice and is a simple technique defined as the product of CAC volume and weighted calcium peak CT attenuation factor (4). In addition, other measures of CAC such as volume, mass (5), number of segments and arteries with CAC (6,7), and calcium density (8,9) were discovered as further predictors of cardiovascular events.Radiomics is a field in biomedical imaging that aims to extract high-dimensional quantitative features from digital images. Several radiomic features or biomarkers have shown predictive value in patients with cancer (10-12). In radiomics studies, it is hypothesized that image voxels contain information that can be converted into meaningful phenotypic characteristics of diseased tissues via computer vision (10,13). Upon image acquisition, the workflow of radiomic analysis includes image segmentation, feature extraction, and informatics and/or machine learning analysis. These features may carry additional information about intensity, shape, and texture and are extracted based on the segmented and masked regions of interest. Some features may be related to routinely used, radiologist-defined semantic characteristics, whereas others may be generally higher order and filtered metrics of different textural characteristics (10,13,14).Thus, we hypothesized that using radiomics may uncover informative multidimensional properties of CAC

Background: Left ventricular (LV) dilatation is a key compensatory feature in patients with chronic aortic regurgitation (AR). However, sex-differences in LV remodeling and outcomes in chronic AR have been poorly investigated so far. Methods: We performed cardiovascular magnetic resonance imaging (CMR) including phase-contrast velocity-encoded imaging for the measurement of regurgitant fraction (RegF) at the sinotubular junction, in consecutive patients with at least mild AR on echocardiography. We assessed LV size (end-diastolic volume indexed to body surface area, LVEDV/BSA) and investigated sex differences between LV remodeling and increasing degrees of AR severity. Cox-regression models were used to test differences in outcomes between men and women using a composite of heart failure hospitalization, unscheduled AR intervention, and cardiovascular death. Results: 270 consecutive patients (59.6% male, 59.8 ± 20.8 y/o, 59.6% with at least moderate AR on echocardiography) were included. On CMR, mean RegF was 18.1 ± 17.9% and a total of 65 (24.1%) had a RegF ≥ 30%. LVEDV/BSA was markedly closer related with AR severity (RegF) in men compared to women. Each 1-SD increase in LVEDV/BSA (mL/m2) was associated with a 9.7% increase in RegF in men and 5.9% in women, respectively (p-value for sex-interaction < 0.001). Based on previously published reference values, women—in contrast to men—frequently had a normal LV size despite severe AR (e.g., for LVEDV/BSA on CMR: 35.3% versus 8.7%, p < 0.001). In a Cox-regression model adjusted for age, LVEDV/BSA and RegF, women were at significantly higher risk for the composite endpoint when compared to men (adj. HR 1.81 (95%CI 1.09–3.03), p = 0.022). Conclusion: In patients with chronic AR, LV remodeling is a hallmark feature in men but not in women. Severity of AR may be underdiagnosed in female patients in the absence of LV dilatation. Future studies need to address the dismal prognosis in female patients with chronic AR.