Standardized measurement of abdominal muscle by computed tomography: association with cardiometabolic risk in the Framingham Heart Study.

OBJECTIVES: To provide a standard for total abdominal muscle mass (TAM) quantification on computed tomography (CT) and investigate its association with cardiovascular risk in a primary prevention setting. METHODS: We included 3016 Framingham Heart Study participants free of cardiovascular disease (CVD) who underwent abdominal CT between 2002 and 2005. On a single CT slice at the level of L3/L4, we segmented (1) TAM-Area, (2) TAM-Index (= TAM-Area/height) and, (3) TAM-Fraction (= TAM-Area/total cross-sectional CT-area). We tested the association of these muscle mass measures with prevalent and incident cardiometabolic risk factors and incident CVD events during a follow-up of 11.0 ± 2.7 years. RESULTS: In this community-based sample (49% women, mean age: 50.0 ± 10.0 years), all muscle quantity measures were significantly associated with prevalent and incident cardiometabolic risk factors and CVD events. However, only TAM-Fraction remained significantly associated with key outcomes (e.g., adj. OR 0.68 [0.55, 0.84] and HR 0.73 [0.57, 0.92] for incident hypertension and CVD events, respectively) after adjustment for age, sex, body mass index, and waist circumference. Moreover, only higher TAM-Fraction was associated with a lower risk (e.g., adj. OR: 0.56 [0.36-0.89] for incident diabetes versus TAM-Area: adj. OR 1.26 [0.79-2.01] and TAM-Index: 1.09 [0.75-1.58]). CONCLUSION: TAM-Fraction on a single CT slice at L3/L4 is a novel body composition marker of cardiometabolic risk in a primary prevention setting that has the potential to improve risk stratification beyond traditional measures of obesity. KEY POINTS: • In this analysis of the Framingham Heart Study (n = 3016), TAM-F on a single slice CT was more closely associated with prevalent and incident cardiometabolic risk factors as compared to TAM alone or TAM indexed to body surface area. • TAM-F on a single abdominal CT slice at the level of L3/L4 could serve as a standard measure of muscle mass and improve risk prediction.

A Noninvasive Assessment of Flow Based on Contrast Dispersion in Computed Tomography Angiography: A Computational and Experimental Phantom Study.

Transluminal attenuation gradient (TAG), defined as the gradient of the contrast agent attenuation drop along the vessel, is an imaging biomarker that indicates stenosis in the coronary arteries. The transluminal attenuation flow encoding (TAFE) equation is a theoretical platform that quantifies blood flow in each coronary artery based on computed tomography angiography (CTA) imaging. This formulation couples TAG (i.e., contrast dispersion along the vessel) with fluid dynamics. However, this theoretical concept has never been validated experimentally. The aim of this proof-of-principle phantom study is to validate TAFE based on CTA imaging. Dynamic CTA images were acquired every 0.5 s. The average TAFE estimated flow rates were compared against four predefined pump values in a straight (20, 25, 30, 35, and 40 ml/min) and a tapered phantom (25, 35, 45, and 55 ml/min). Using the TAFE formulation with no correction, the flow rates were underestimated by 33% and 81% in the straight and tapered phantoms, respectively. The TAFE formulation was corrected for imaging artifacts focusing on partial volume averaging and radial variation of contrast enhancement. After corrections, the flow rates estimated in the straight and tapered phantoms had an excellent Pearson correlation of r = 0.99 and 0.87 (p < 0.001), respectively, with only a 0.6%±0.2 mL/min difference in estimation of the flow rate. In this proof-of-concept phantom study, we corrected the TAFE formulation and showed a good agreement with the actual pump values. Future clinical validations are needed for feasibility of TAFE in clinical use.

Small whole heart volume predicts cardiovascular events in patients with stable chest pain: insights from the PROMISE trial.

OBJECTIVES: The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT). METHODS: Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac. We compared the WHV across categories of cardiovascular risk factors and coronary artery disease (CAD) characteristics and determined the association of WHV with MACE (all-cause death, myocardial infarction, unstable angina; median follow-up: 26 months). RESULTS: In the 3798 included patients (60.5 ± 8.2 years; 51.5% women), the WHV was 351.9 ± 57.6 cm3/m2. We found smaller WHV in no- or non-obstructive CAD, women, people with diabetes, sedentary lifestyle, and metabolic syndrome. Larger WHV was found in obstructive CAD, men, and increased atherosclerosis cardiovascular disease (ASCVD) risk score (p < 0.05). In a time-to-event analysis, small WHV was associated with over 4.4-fold risk of MACE (HR (per one standard deviation) = 0.221; 95% CI: 0.068-0.721; p = 0.012) independent of ASCVD risk score and CT-derived CAD characteristics. In patients with non-obstructive CAD, but not in those with no- or obstructive CAD, WHV increased the discriminatory capacity of ASCVD and CT-derived CAD characteristics significantly. CONCLUSIONS: Small WHV may represent a novel imaging marker of MACE in stable chest pain. In particular, WHV may improve risk stratification in patients with non-obstructive CAD, a cohort with an unmet need for better risk stratification. KEY POINTS: • Heart volume is easily assessable from non-contrast cardiac computed tomography. • Small heart volume may be an imaging marker of major adverse cardiac events independent and incremental to traditional cardiovascular risk factors and established CT measures of CAD. • Heart volume may improve cardiovascular risk stratification in patients with non-obstructive CAD.

Effect of Wall Elasticity on Hemodynamics and Wall Shear Stress in Patient-Specific Simulations in the Coronary Arteries.

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.

Density and morphology of coronary artery calcium for the prediction of cardiovascular events: insights from the Framingham Heart Study.

OBJECTIVES: To investigate the association between directly measured density and morphology of coronary artery calcium (CAC) with cardiovascular disease (CVD) events, using computed tomography (CT). METHODS: Framingham Heart Study (FHS) participants with CAC in noncontrast cardiac CT (2002-2005) were included and followed until 2016. Participants with known CVD or uninterpretable CT scans were excluded. We assessed and correlated (Spearman) CAC density, CAC volume, and the number of calcified segments. Moreover, we counted morphology features including shape (cylindrical, spherical, semi-tubular, and spotty), location (bifurcation, facing pericardium, or facing myocardium), and boundary regularity. In multivariate Cox regression analyses, we associated all CAC characteristics with CVD events (CVD-death, myocardial infarction, stroke). RESULTS: Among 1330 included participants (57.8 ± 11.7 years; 63% male), 73 (5.5%) experienced CVD events in a median follow-up of 9.1 (7.8-10.1) years. CAC density correlated strongly with CAC volume (Spearman's ρ = 0.75; p < 0.001) and lower number of calcified segments (ρ = - 0.86; p < 0.001; controlled for CAC volume). In the survival analysis, CAC density was associated with CVD events independent of Framingham risk score (HR (per SD) = 2.09; 95%CI, 1.30-3.34; p = 0.002) but not after adjustment for CAC volume (p = 0.648). The extent of spherically shaped and pericardially sided calcifications was associated with fewer CVD events accounting for the number of calcified segments (HR (per count) = 0.55; 95%CI, 0.31-0.98; p = 0.042 and HR = 0.66; 95%CI, 0.45-0.98; p = 0.039, respectively). CONCLUSIONS: Directly measured CAC density does not predict CVD events due to the strong correlation with CAC volume. The spherical shape and pericardial-sided location of CAC are associated with fewer CVD events and may represent morphological features related to stable coronary plaques. KEY POINTS: • Coronary calcium density may not be independently associated with cardiovascular events. • Coronary calcium density correlates strongly with calcium volume. • Spherical shape and pericardial-sided location of CAC are associated with fewer CVD events.

Flow Dynamics in the Aortic Arch and Its Effect on the Arterial Input Function in Cardiac Computed Tomography.

The arterial input function (AIF)-time-density curve (TDC) of contrast at the coronary ostia-plays a central role in contrast enhanced computed tomography angiography (CTA). This study employs computational modeling in a patient-specific aorta to investigate mixing and dispersion of contrast in the aortic arch (AA) and to compare the TDCs in the coronary ostium and the descending aorta. Here, we examine the validity of the use of TDC in the descending aorta as a surrogate for the AIF. Computational fluid dynamics (CFD) was used to study hemodynamics and contrast dispersion in a CTA-based patient model of the aorta. Variations in TDC between the aortic root, through the AA and at the descending aorta and the effect of flow patterns on contrast dispersion was studied via postprocessing of the results. Simulations showed complex unsteady patterns of contrast mixing and dispersion in the AA that are driven by the pulsatile flow. However, despite the relatively long intra-aortic distance between the coronary ostia and the descending aorta, the TDCs at these two locations were similar in terms of rise-time and up-slope, and the time lag between the two TDCs was 0.19 s. TDC in the descending aorta is an accurate analog of the AIF. Methods that use quantitative metrics such as rise-time and slope of the AIF to estimate coronary flowrate and myocardial ischemia can continue with the current practice of using the TDC at the descending aorta as a surrogate for the AIF.

Computational Study of Computed Tomography Contrast Gradients in Models of Stenosed Coronary Arteries.

Recent computed tomography coronary angiography (CCTA) studies have noted higher transluminal contrast agent gradients in arteries with stenotic lesions, but the physical mechanism responsible for these gradients is not clear. We use computational fluid dynamics (CFD) modeling coupled with contrast agent dispersion to investigate the mechanism for these gradients. Simulations of blood flow and contrast agent dispersion in models of coronary artery are carried out for both steady and pulsatile flows, and axisymmetric stenoses of severities varying from 0% (unobstructed) to 80% are considered. Simulations show the presence of measurable gradients with magnitudes that increase monotonically with stenotic severity when other parameters are held fixed. The computational results enable us to examine and validate the hypothesis that transluminal contrast gradients (TCG) are generated due to the advection of the contrast bolus with time-varying contrast concentration that appears at the coronary ostium. Since the advection of the bolus is determined by the flow velocity in the artery, the magnitude of the gradient, therefore, encodes the coronary flow velocity. The correlation between the flow rate estimated from TCG and the actual flow rate in the computational model of a physiologically realistic coronary artery is 96% with a R2 value of 0.98. The mathematical formulae connecting TCG to flow velocity derived here represent a novel and potentially powerful approach for noninvasive estimation of coronary flow velocity from CT angiography.

Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes.

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver-derived transcriptomics datasets (ntotal = 137) which enabled ML-methods to classify spaceflown vs. ground control animals (AUC ≥ 0.87) while mitigating the variability from mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.

Deep neural networks detect regional wall motion abnormalities and preclinical cardiovascular disease from 12-lead ECGs.

Background: Identifying regional wall motion abnormalities (RWMAs) is critical for diagnosing and risk stratifying patients with cardiovascular disease, particularly ischemic heart disease. We hypothesized that a deep neural network could accurately identify patients with regional wall motion abnormalities from a readily available standard 12-lead electrocardiogram (ECG). Methods: This observational, retrospective study included patients who were treated at Beth Israel Deaconess Medical Center and had an ECG and echocardiogram performed within 14 days of each other between 2008 and 2019. We trained a convolutional neural network to detect the presence of RWMAs, qualitative global right ventricular (RV) hypokinesis, and varying degrees of left ventricular dysfunction (left ventricular ejection fraction [LVEF] ≤50%, LVEF ≤40%, and LVEF ≤35%) identified by echocardiography, using ECG data alone. Patients were randomly split into development (80%) and test sets (20%). Model performance was assessed using area under the receiver operating characteristic curve (AUC). Cox proportional hazard models adjusted for age and sex were performed to estimate the risk of future acute coronary events. Results: The development set consisted of 19,837 patients (mean age 66.7±16.4; 46.7% female) and the test set comprised of 4,953 patients (mean age 67.5±15.8 years; 46.5% female). On the test dataset, the model accurately identified the presence of RWMA, RV hypokinesis, LVEF ≤50%, LVEF ≤40%, and LVEF ≤35% with AUCs of 0.87 (95% CI 0.858-0.882), 0.888 (95% CI 0.878-0.899), 0.923 (95% CI 0.914-0.933), 0.93 (95% CI 0.921-0.939), and 0.876 (95% CI 0.858-0.896), respectively. Among patients with normal biventricular function at the time of the index ECG, those classified as having RMWA by the model were 3 times the risk (age- and sex-adjusted hazard ratio, 2.8; 95% CI 1.9-3.9) for future acute coronary events compared to those classified as negative. Conclusions: We demonstrate that a deep neural network can help identify regional wall motion abnormalities and reduced LV function from a 12-lead ECG and could potentially be used as a screening tool for triaging patients who need either initial or repeat echocardiographic imaging.

Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers.

BACKGROUND: Although regional wall motion abnormality (RWMA) detection is foundational to transthoracic echocardiography, current methods are prone to interobserver variability. We aimed to develop a deep learning (DL) model for RWMA assessment and compare it to expert and novice readers. METHODS: We used 15,746 transthoracic echocardiography studies-including 25,529 apical videos-which were split into training, validation, and test datasets. A convolutional neural network was trained and validated using apical 2-, 3-, and 4-chamber videos to predict the presence of RWMA in 7 regions defined by coronary perfusion territories, using the ground truth derived from clinical transthoracic echocardiography reports. Within the test cohort, DL model accuracy was compared to 6 expert and 3 novice readers using F1 score evaluation, with the ground truth of RWMA defined by expert readers. Significance between the DL model and novices was assessed using the permutation test. RESULTS: Within the test cohort, the DL model accurately identified any RWMA with an area under the curve of 0.96 (0.92-0.98). The mean F1 scores of the experts and the DL model were numerically similar for 6 of 7 regions: anterior (86 vs 84), anterolateral (80 vs 74), inferolateral (83 vs 87), inferoseptal (86 vs 86), apical (88 vs 87), inferior (79 vs 81), and any RWMA (90 vs 94), respectively, while in the anteroseptal region, the F1 score of the DL model was lower than the experts (75 vs 89). Using F1 scores, the DL model outperformed both novices 1 (P = .002) and 2 (P = .02) for the detection of any RWMA. CONCLUSIONS: Deep learning provides accurate detection of RWMA, which was comparable to experts and outperformed a majority of novices. Deep learning may improve the efficiency of RWMA assessment and serve as a teaching tool for novices.

Associations of Muscle Density and Area With Coronary Artery Plaque and Physical Function.

OBJECTIVE: Skeletal muscle quality and mass are important for maintaining physical function during advancing age. We leveraged baseline data from Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) to evaluate whether paraspinal muscle density and muscle area are associated with cardiac or physical function outcomes in people with HIV (PWH). METHODS: REPRIEVE is a double-blind randomized trial evaluating the effect of pitavastatin for primary prevention of major adverse cardiovascular events in PWH. This cross-sectional analysis focuses on participants who underwent coronary computed tomography at baseline. Lower thoracic paraspinal muscle density (Hounsfield units [HU]) and area (cm 2 ) were assessed on noncontrast computed tomography. RESULTS: Of 805 PWH, 708 had paraspinal muscle measurements. The median age was 51 years and 17% were natal female patients. The median muscle density was 41 HU (male) and 30 HU (female); area 13.2 cm 2 /m (male) and 9.9 cm 2 /m (female). In adjusted analyses, greater density (less fat) was associated with a lower prevalence of any coronary artery plaque, coronary artery calcium score >0, and high plaque burden ( P = 0.06); area was not associated with plaque measures. Among 139 patients with physical function measures, greater area (but not density) was associated with better performance on a short physical performance battery and grip strength. CONCLUSIONS: Among PWH, greater paraspinal muscle density was associated with a lower prevalence of coronary artery disease while greater area was associated with better physical performance. Whether changes in density or area are associated with changes in CAD or physical performance will be evaluated through longitudinal analyses in REPRIEVE.

A multi-omics longitudinal study of the murine retinal response to chronic low-dose irradiation and simulated microgravity.

The space environment includes unique hazards like radiation and microgravity which can adversely affect biological systems. We assessed a multi-omics NASA GeneLab dataset where mice were hindlimb unloaded and/or gamma irradiated for 21 days followed by retinal analysis at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles resulting in a total of 4178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation difference was seen across different conditions at the same time point. Nucleotide metabolism biological processes were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. A total of 23 genes showed significant changes in methylation and expression compared to unexposed controls, including genes involved in retinal function and inflammatory response. This multi-omics analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination and highlights important molecular mechanisms at different post-exposure stages.

Validation of Wall Shear Stress Assessment in Non-invasive Coronary CTA versus Invasive Imaging: A Patient-Specific Computational Study.

Endothelial shear stress (ESS) identifies coronary plaques at high risk for progression and/or rupture leading to a future acute coronary syndrome. In this study an optimized methodology was developed to derive ESS, pressure drop and oscillatory shear index using computational fluid dynamics (CFD) in 3D models of coronary arteries derived from non-invasive coronary computed tomography angiography (CTA). These CTA-based ESS calculations were compared to the ESS calculations using the gold standard with fusion of invasive imaging and CTA. In 14 patients paired patient-specific CFD models based on invasive and non-invasive imaging of the left anterior descending (LAD) coronary arteries were created. Ten patients were used to optimize the methodology, and four patients to test this methodology. Time-averaged ESS (TAESS) was calculated for both coronary models applying patient-specific physiological data available at the time of imaging. For data analysis, each 3D reconstructed coronary artery was divided into 2 mm segments and each segment was subdivided into 8 arcs (45°).TAESS and other hemodynamic parameters were averaged per segment as well as per arc. Furthermore, the paired segment- and arc-averaged TAESS were categorized into patient-specific tertiles (low, medium and high). In the ten LADs, used for optimization of the methodology, we found high correlations between invasively-derived and non-invasively-derived TAESS averaged over segments (n = 263, r = 0.86) as well as arcs (n = 2104, r = 0.85, p < 0.001). The correlation was also strong in the four testing-patients with r = 0.95 (n = 117 segments, p = 0.001) and r = 0.93 (n = 936 arcs, p = 0.001).There was an overall high concordance of 78% of the three TAESS categories comparing both methodologies using the segment- and 76% for the arc-averages in the first ten patients. This concordance was lower in the four testing patients (64 and 64% in segment- and arc-averaged TAESS). Although the correlation and concordance were high for both patient groups, the absolute TAESS values averaged per segment and arc were overestimated using non-invasive vs. invasive imaging [testing patients: TAESS segment: 30.1(17.1-83.8) vs. 15.8(8.8-63.4) and TAESS arc: 29.4(16.2-74.7) vs 15.0(8.9-57.4) p < 0.001]. We showed that our methodology can accurately assess the TAESS distribution non-invasively from CTA and demonstrated a good correlation with TAESS calculated using IVUS/OCT 3D reconstructed models.

Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer.

Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data (n = 858) with manual heart segmentations provided by cardiovascular radiologists. Validation of the system was performed in an independent real-world dataset of 5677 breast cancer patients treated with radiation therapy at the Dana-Farber/Brigham and Women's Cancer Center between 2008-2018. In a subset of 20 patients, the performance of the system was compared to eight radiation oncology experts by assessing segmentation time, agreement between experts, and accuracy with and without deep-learning assistance. To compare the performance to segmentations used in the clinic, concordance and failures (defined as Dice < 0.85) of the system were evaluated in the entire dataset. The system was successfully applied without retraining. With deep-learning assistance, segmentation time significantly decreased (4.0 min [IQR 3.1-5.0] vs. 2.0 min [IQR 1.3-3.5]; p < 0.001), and agreement increased (Dice 0.95 [IQR = 0.02]; vs. 0.97 [IQR = 0.02], p < 0.001). Expert accuracy was similar with and without deep-learning assistance (Dice 0.92 [IQR = 0.02] vs. 0.92 [IQR = 0.02]; p = 0.48), and not significantly different from deep-learning-only segmentations (Dice 0.92 [IQR = 0.02]; p ≥ 0.1). In comparison to real-world data, the system showed high concordance (Dice 0.89 [IQR = 0.06]) across 5677 patients and a significantly lower failure rate (p < 0.001). These results suggest that deep-learning algorithms can successfully be applied across medical specialties and improve clinical care beyond the original field of interest.

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

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.

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling.

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information.

Sex Differences in Left Ventricular Remodeling and Outcomes in Chronic Aortic Regurgitation.

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.

Radiomics of Coronary Artery Calcium in the Framingham Heart Study.

PURPOSE: To extract radiomic features from coronary artery calcium (CAC) on CT images and to determine whether this approach could improve the ability to identify individuals at risk for a composite endpoint of clinical events. MATERIALS AND METHODS: Participants from the Offspring and Third Generation cohorts of the community-based Framingham Heart Study underwent noncontrast cardiac CT (2002-2005) and were followed for more than a median of 9.1 years for composite major events. A total of 624 participants with CAC Agatston score (AS) of greater than 0 and good or excellent CT image quality were included for manual CAC segmentation and extraction of a predefined set of radiomic features reflecting intensity, shape, and texture. In a discovery cohort (n = 318), machine learning was used to select the 20 most informative and nonredundant CAC radiomic features, classify features predicting events, and define a radiomic-based score (RS). Performance of the RS was tested independently for the prediction of events in a validation cohort (n = 306). RESULTS: The RS had a median value of 0.08 (interquartile range, 0.007-0.71) and a weak and modest correlation with Framingham risk score (FRS) (r = 0.2) and AS (r = 0.39), respectively. The continuous RS unadjusted, adjusted for age and sex, FRS, AS, and FRS plus AS were significantly associated with events (hazard ratio [HR] = 2.2, P < .001; HR = 1.8, P = .002; HR = 2.0, P < .001; HR = 1.7, P = .02; and HR = 1.8, P = .01, respectively). In participants with AS of less than 300, RS association with events remained significant when unadjusted and adjusted for age and sex, FRS, AS, and FRS plus AS (HR = 2.4, 2.8, 2.8, 2.3, and 2.6; P < .001, respectively). In the same subgroup of participants, adding the RS to AS resulted in a significant improvement in the discriminatory ability for events as compared with the AS (area under the receiver operating curve: 0.80 vs 0.68, respectively; P = .03). CONCLUSION: A radiomic-based score, including the complex properties of CAC, may constitute an imaging biomarker to be further developed to identify individuals at risk for major adverse cardiovascular events in a community-based cohort. Supplemental material is available for this article. © RSNA, 2020.

Cardiac Computed Tomography - More Than Coronary Arteries? A Clinical Update.

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: · Coronary artery imaging remains the main indication for cardiac CT. · Novel computational fluid dynamics allow the calculation of a CT-derived fractional flow reserve in patients with known or suspected coronary artery disease. · Cardiac CT delivers information on left ventricular volume as well as myocardial function and perfusion. · CT is the cardinal element for pre-interventional planning in transcatheter valve implantation and pulmonary vein isolation. CITATION FORMAT: · Taron J, Foldyna B, Eslami P et al. Cardiac Computed Tomography - More Than Coronary Arteries? A Clinical Update. Fortschr Röntgenstr 2019; 191: 817 - 826.

A Highly Automated Computational Method for Modeling of Intracranial Aneurysm Hemodynamics.

Intracranial aneurysms manifest in a vast variety of morphologies and their growth and rupture risk are subject to patient-specific conditions that are coupled with complex, non-linear effects of hemodynamics. Thus, studies that attempt to understand and correlate rupture risk to aneurysm morphology have to incorporate hemodynamics, and at the same time, address a large enough sample size so as to produce reliable statistical correlations. In order to perform accurate hemodynamic simulations for a large number of aneurysm cases, automated methods to convert medical imaging data to simulation-ready configuration with minimal (or no) human intervention are required. In the present study, we develop a highly-automated method based on the immersed boundary method framework to construct computational models from medical imaging data which is the key idea is the direct use of voxelized contrast information from the 3D angiograms to construct a level-set based computational "mask" for the hemodynamic simulation. Appropriate boundary conditions are provided to the mask and the dynamics of blood flow inside the vessels and aneurysm is simulated by solving the Navier-Stokes equations on the Cartesian grid using the sharp-interface immersed boundary method. The present method does not require body conformal surface/volume mesh generation or other intervention for model clean-up. The viability of the proposed method is demonstrated for a number of distinct aneurysms derived from actual, patient-specific data.