Non-contrast Cardiac Computed Tomography Research Articles

Significant impact of a novel whole heart motion correction algorithm on cardiac computed tomography and aortic valve calcium scores

Abstract Introduction A new whole-heart motion-correction algorithm has been available in cardiac computed tomography (CT), and it has already revealed the higher diagnostic accuracy of significant coronary artery stenosis in cases with higher heart rate at the CT scan. Recently, aortic valve calcium scores (AVCS) are often measured for the decision-making of surgical treatment of aortic valve stenosis. However, the measurement of AVCS of the aortic valve has potential risks of motion artifact of the aortic valve. Therefore, we would like to know the clinical impact of a new whole-heart motion correction algorithm for accurately measuring aortic valve AVCS without motion artifacts. Purpose The purpose of this study is to evaluate the impact of a new whole-heart motion-correction algorithm on non-contrast cardiac CT images and the AVCS in trans-catheter aortic valve regurgitation (TAVR) candidates with severe aortic valve stenosis. Materials and methods Forty-two consecutive TAVR candidates (83 ± 7 years old, 21 males) who underwent ECG-gated CT using 256-row detector CT from November 2023 to February 2024 at our hospital were analyzed. The best phase non-contrast cardiac images were automatically selected, and they were reconstructed with both the standard (STD) algorithm and the new whole-heart motion-correction algorithm. AVCS was calculated using semi-automatic commercially available software. The aortic valve's image noise was also measured. Results Non-contrast best cardiac phase images were successfully reconstructed using the new motion correction algorithm in 36 patients (86%). The best cardiac phase was mainly the diastolic phase in 29 patients (81%). The overall Agatston score and calcified volume of the aortic valve in SSF2 were 1779 ± 896, and 1403 ± 689, respectively, and they were lower than 2195 ± 1132 and 1656 ± 851 of STD (all P < 0.01). Conclusion The new whole-heart motion-correction algorithm worked in almost all TAVR candidates, and revealed lower AVCS and shows a potential for increasing AVCS accuracy.

AI-based algorithm for assessing coronary artery calcium score on contrast-enhanced cardiac computed tomography scans

Abstract Introduction In cardiac computed tomography (CT) examinations, non-contrast scans are often performed in addition to contrast-enhanced scans to quantify the coronary artery calcification (CAC) score. These calcium score scans are associated with additional exposure to ionizing radiation. Purpose We sought to develop a fully automated artificial intelligence (AI)-based algorithm capable of determining CAC score based solely on contrast-enhanced CT scans, eliminating the need for additional non-contrast scans. Methods An automated CAC scoring algorithm on contrast-enhanced CT scans was developed and trained using a dataset of 297 cardiac CT studies. Coronary artery calcifications were manually segmented in contrast-enhanced scans using a threshold of 2 standard deviations above the mean attenuation value of the ascending aorta. On non-contrast scans, CAC was manually assessed using a standard threshold of ≥130 Hounsfield Units. A correction factor for calcium score results assessed on contrast-enhanced scans was determined using linear regression. The algorithm was tested on an independent set of 90 contrast-enhanced CT scans (four manufacturers, eight scanner models). We compared automated CAC scores to manual expert reader reference assessments on non-contrast CT scans. CAC scores were categorized into four risk categories following the Society of Cardiovascular Computed Tomography recommendations: 0, 1-100, 101-300, and >300 Agatston Units. Results In the assessment of 90 CT studies (mean age 61.0±11.4 years, 46.7% males), the AI-model detected CAC in 69 contrast-enhanced scans (76.7%), comparable to the human reader's detection rate of CAC in 71 non-contrast scans (78.9%) (p = 0.63). The CAC score was initially calculated using AI-based segmentation of coronary calcification on contrast-enhanced scans and then the result was multiplied by established linear correction factor of 1.97. There was an excellent correlation between AI-model and manual reference total CAC scores (Pearson’s r = 0.96, 95% CI 0.94–0.97, p < 0.001). The model correctly classified 77 patients (85.6%) into the same CAC risk category as the human reader (Figure 1). Among 19 patients (21.1%) with a CAC score of zero, only 1 patient (5.3%) was reclassified with a non-zero CAC score by the AI-model. Cohen’s kappa value for CAC score risk categorization was 0.80 (p < 0.001), indicating very good agreement (Figure 2). Bland–Altman analysis revealed minimal bias of -9.7 Agatston Unit with 95% limits of agreement ranging from -184.8 to 165.5 Agatston Unit. Conclusions CAC score can be accurately quantified on contrast-enhanced cardiac CT scans using an automated AI-based algorithm. This approach has the potential to eliminate the necessity for an additional non-contrast CT scan, thereby reducing the patient's exposure to ionizing radiation.

Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images

Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for classifying the patients into four TAC risk categories. The method started by segmenting the thoracic aorta, combining three UNets trained with axial, sagittal and coronal CT images. Afterwards, the surrounding lesion candidates were classified using three combined convolutional neural networks (CNNs) trained with orthogonal patches. Image datasets included 1190 non-enhanced ECG-gated cardiac CT studies from a cohort of cardiovascular patients (age 57 ± 9 years, 80% men, 65% TAC > 0). In the test set (N = 119), the combination of UNets was able to successfully segment the thoracic aorta with a mean volume difference of 0.3 ± 11.7 ml (<6%) and a median Dice coefficient of 0.947. The combined CNNs accurately classified the lesion candidates and 87% of the patients (N = 104) were accurately placed in their corresponding risk categories (Kappa = 0.826, ICC = 0.9915). TAC measurement can be estimated automatically from cardiac CT images using UNets to isolate the thoracic aorta and CNNs to classify calcified lesions.

The Diagnostic Yield and Clinical Impact of Systematic Screening of Kidney Transplant Candidates by Cardiac Computed Tomography: A Cohort Study

Although cardiovascular screening of kidney transplant candidates is recommended, the optimal approach is debated. Previous studies show that noninvasive imaging provides prognostic information, but systematic screening may have less recognized effects, such as additional investigations, incidental findings, procedural complications, and delay of transplantation. To address this, we characterized the diagnostic yield and clinical implications of systematic screening for cardiovascular disease using cardiac computed tomography (CT) in potential kidney transplant candidates. This was a single-center, observational cohort study including all potential kidney transplant candidates >40 years of age or with diabetes or on dialysis treatment for >5 years, systematically referred to cardiac computed tomography (CT; non-contrast CT and coronary CT angiography) between 2014 and 2019 before evaluation for kidney transplantation at Aarhus University Hospital. Patient records were examined for data on baseline characteristics, additional investigations and complications, plasma creatinine, dialysis initiation, time until wait-listing, and incidental findings. Of 473 patients who underwent cardiac CT, additional cardiac investigations were performed in 156 (33%), and 32 (7%) were revascularized. Twenty-two patients had significant incidental nonvascular findings on cardiac CT. No patient was rejected for transplantation based on cardiac CT. In patients not yet on dialysis, the slope in the estimated glomerular filtration rate decline did not change significantly after coronary CT angiography. Screening by cardiac CT led to additional cardiac investigations in one-third of patients; only a few patients were revascularized, with unknown benefits in asymptomatic patients. Cardiac CT was safe in this population; however, the clinical consequences of the screening were limited.

Methods and Reproducibility of Liver Fat Measurement Using 3-Dimensional Liver Segmentation From Noncontrast Computed Tomography in EVAPORATE Cohort.

Nonalcoholic fatty liver disease not only shares multiple risk factors with cardiovascular disease but also independently predicts its increased risk and related outcomes. Here, we evaluate reproducibility of 3-dimensional (3D) liver volume segmentation method to identify fatty liver on noncontrast cardiac computed tomography (CT) and compare measures with previously validated 2-dimensional (2D) segmentation CT criteria for the measurement of liver fat. The study included 68 participants enrolled in the EVAPORATE trial and underwent serial noncontrast cardiac CT. Liver attenuation < 40 Hounsfield units (HU) was used for diagnosing fatty liver, as done in the MESA study. Two-dimensional and 3D segmentation of the liver were performed by Philips software. Bland-Altman plot analysis was used to assess reproducibility. Interreader reproducibility of 3D liver mean HU measurements was 96% in a sample of 111 scans. Reproducibility of 2D and 3D liver mean HU measurements was 93% in a sample of 111 scans. Reproducibility of change in 2D and 3D liver mean HU was 94% in 68 scans. Kappa, a measure of agreement in which the 2D and 3D measures both identified fatty liver, was excellent at 96.4% in 111 scans. Fatty liver can be reliably diagnosed and measured serially in a stable and reproducible way by 3D liver segmentation of noncontrast cardiac CT scans. Future studies need to explore the sensitivity and stability of measures for low liver fat content by 3D segmentation, over the current 2D methodology. This measure can serve as an imaging biomarker to understand mechanistic correlations between atherosclerosis, fatty liver, and cardiovascular disease risk.

Non-Contrast and Contrast-Enhanced Cardiac Computed Tomography Imaging in the Diagnostic and Prognostic Evaluation of Coronary Artery Disease.

In recent decades, cardiac computed tomography (CT) has emerged as a powerful non-invasive tool for risk stratification, as well as the detection and characterization of coronary artery disease (CAD), which remains the main cause of morbidity and mortality in the world. Advances in technology have favored the increasing use of cardiac CT by allowing better performance with lower radiation doses. Coronary artery calcium, as assessed by non-contrast CT, is considered to be the best marker of subclinical atherosclerosis, and its use is recommended for the refinement of risk assessment in low-to-intermediate risk individuals. In addition, coronary CT angiography (CCTA) has become a gate-keeper to invasive coronary angiography (ICA) and revascularization in patients with acute chest pain by allowing the assessment not only of the extent of lumen stenosis, but also of its hemodynamic significance if combined with the measurement of fractional flow reserve or perfusion imaging. Moreover, CCTA provides a unique incremental value over functional testing and ICA by imaging the vessel wall, thus allowing the assessment of plaque burden, composition, and instability features, in addition to perivascular adipose tissue attenuation, which is a marker of vascular inflammation. There exists the potential to identify the non-obstructive lesions at high risk of progression to plaque rupture by combining all of these measures.

The impact of iodine contrast agent on radiation dose of heart and blood: a comparison between coronary CT angiography and cardiac calcium scoring CT.

Iodine contrast agent (CA) is widely used in cardiac computed tomography (CT). The CA can increase the organ radiation doses due to the photoelectric effect. To investigate the impact of CA on radiation dose in cardiac CT by comparing the radiation dose between contrast coronary CT angiography (CCTA) and non-contrast calcium scoring CT (CSCT). Radiation doses were computationally calculated for 30 individual patients who received CSCT and CCTA in the same exam session. The geometry and acquisition parameters were modeled in the simulations based on individual patient CT images and acquisitions. Doses in the presence and absence of CA were obtained in the aorta, left ventricle (LV), right ventricle (RV), and myocardial tissue (MT). The dose values were normalized by size-specific dose estimate (SSDE). The dose enhancement factors (DEFSSDE) were calculated as the ratio of doses in CCTA over doses in CSCT. Compared to the CSCT scans, doses increase in the CCTA scans in the aorta (DEFSSDE = 2.14 ± 0.20), LV (DEFSSDE = 1.78 ± 0.26), and RV (DEFSSDE = 1.31 ± 0.22). A linear relation is observed between the local CA concentrations and the dose increase in the heart; DEFSSDE = 0.07*I(mg/mL) + 0.80 (R2 = 0.8; p < 0.01). The DEFSSDE in the MT (DEFSSDE = 0.96 ± 0.08) showed no noticeable impact of CA on the dose in this tissue. In addition, patient variability in the dose distributions was observed. A linear causal relation exists between local CA concentration and increase in radiation dose in cardiac CT. For the same CT exposure, dose to the heart is on average 55% higher in contrast cardiac CT.

Epicardial adipose tissue volume and density is associated with cardiac dysfunction in asymptomatic people with type 2 diabetes

Abstract Background Type 2 diabetes (T2D) is associated with several perturbations of cardiac structure and function, which are precursors to the development of heart failure. Excess accumulation of epicardial adipose tissue (EAT) may contribute to cardiac dysfunction in individuals with T2D. Lipid-laden adipocytes have a lower computed tomography (CT) attenuation and can be readily identified using cardiac CT. Using a multimodality cardiac imaging approach, we aimed to assess the association of total and low attenuation EAT volume with early markers of cardiac dysfunction in people with T2D. Methods Prospective case-control study, in which participants with and without T2D and no known cardiovascular disease, underwent comprehensive cardiovascular phenotyping including multiparametric cardiac magnetic resonance imaging (MRI), echocardiography and non-contrast cardiac CT. EAT volume was measured from CT scans using a deep learning method and volumes indexed to body surface area. Total EAT was defined according to CT adipose tissue attenuation range of −30 to −190 Hounsfield Units (HU) and low attenuation EAT as −90 to −190 HU. Left ventricular (LV) volumes, function and strain measurements were derived from cardiac MRI images and diastolic function also assessed using echocardiography. Markers of early cardiac dysfunction in those with T2D were assessed for associations with EAT in T2D participants using multivariable linear regression analyses. Results Two hundred and fifty-four participants were included: demographic, anthropometric and imaging variables are displayed in Table 1. Subjects with T2D had increased LV concentric remodelling (higher LV mass/volume ratio), diastolic dysfunction (lower circumferential peak early diastolic strain rate (PEDSR) and average E/e') and reduced systolic function (global longitudinal strain, GLS) compared with controls. Total and low attenuation indexed EAT volumes were 1.6-fold and 2-fold higher, respectively, in participants with T2D compared to controls (Figure 1). After adjustment for age, gender, ethnicity, insulin resistance, systolic blood pressure and waist/hip ratio, total and low attenuation indexed EAT volume were independently associated with LV mass/volume ratio (total indexed EAT volume: β=0.002, p=0.02, low attenuation indexed EAT volume: β=0.004, p=0.01) and LV GLS (total indexed EAT volume: β=−0.02, p&amp;lt;0.01, low attenuation indexed EAT volume: β=−0.04, p=0.02) in subjects with T2D, but not indices of diastolic dysfunction. Conclusion Total and low attenuation EAT volumes are higher in individuals with T2D, and excess EAT accumulation is independently associated with early markers of cardiac dysfunction. Further studies into the underlying mechanisms of this interaction may facilitate the development of interventions targeted at EAT, which could mitigate against the development of heart failure in people with T2D. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): United Kingdom National Institute for Health Research

Machine Learning for the Prevalence and Severity of Coronary Artery Calcification in Nondialysis Chronic Kidney Disease Patients: A Chinese Large Cohort Study.

Purpose:This study sought to determine whether machine learning (ML) can be used to better identify the risk factors and establish the prediction models for the prevalence and severity of coronary artery calcification (CAC) in nondialysis chronic kidney disease (CKD) patients and compare the performance of distinctive ML models with conventional logistic regression (LR) model.Materials and Methods:In all, 3701 Chinese nondialysis CKD patients undergoing noncontrast cardiac computed tomography (CT) scanning were enrolled from November 2013 to December 2017. CAC score derived from the cardiac CT was calculated with the calcium scoring software and was used to assess and stratify the prevalence and severity of CAC. Four ML models (LR, random forest, support vector machine, and k-nearest neighbor) and the corresponding feature ranks were conducted. The model that incorporated the independent predictors was shown as the receiver-operating characteristic (ROC) curve. Area under the curve (AUC) was used to present the prediction value. ML model performance was compared with the traditional LR model using pairwise comparisons of AUCs.Results:Of the 3701 patients, 943 (25.5%) patients had CAC. Of the 943 patients with CAC, 764 patients (20.6%) and 179 patients (4.8%) had an Agatston CAC score of 1 to 300 and ≥300, respectively. The primary cohort and the independent validation cohort comprised 2957 patients and 744 patients, respectively. For the prevalence of CAC, the AUCs of ML models were from 0.78 to 0.82 in the training data set and the internal validation cohort. For the severity of CAC, the AUCs of the 4 ML models were from 0.67 to 0.70 in the training data set and from 0.53 to 0.70 in the internal validation cohort. For the prevalence of CAC, the AUC was 0.80 (95% confidence interval [CI]: 0.77-0.83) for ML (LR) versus 0.80 (95% CI: 0.77-0.83) for the traditional LR model (P=0.2533). For the severity of CAC, the AUC was 0.70 (95% CI: 0.63-0.77) for ML (LR) versus 0.70 (95% CI: 0.63-0.77) for traditional LR model (P=0.982).Conclusions:This study constructed prediction models for the presence and severity of CAC based on Agatston scores derived from noncontrast cardiac CT scanning in nondialysis CKD patients using ML, and showed ML LR had the best performance.

An automated quantification method for the Agatston coronary artery calcium score on coronary computed tomography angiography.

A coronary artery calcium (CAC) score can provide supplementary information for predicting the risk of cardiovascular disease (CVD). Although CAC is clinically measured with non-contrast cardiac computed tomography (CT), coronary CT angiography (CCTA) may also be used, allowing for the simultaneous evaluation of coronary artery vessels and calcified plaques. This study proposes a method for the automated quantification of the Agatston CAC score from CCTA and compares our method's performance with that of non-contrast cardiac CT. Sixty-two patients were selected from a clinical registry and divided into four CAC categories. They underwent both non-contrast cardiac CT and CCTA. The Agatston CAC score derived from non-contrast cardiac CT (standard Agatston CAC score) was used as the reference standard. Calcifications were automatically identified and quantified using different thresholds after a deep learning-based coronary artery segmentation model pretrained on CCTA images. Comparisons were made between the standard Agatston CAC score and the CCTA-based Agatston CAC score (CCTA-CAC score) on a per-patient and per-vessel basis. Spearman's rank-order correlation coefficient (R) and intra-class correlation (ICC) values were used to calculate the correlation between the two methods. After comparison, the optimal lower threshold in CCTA-CAC score calculations was found to be 650 Hounsfield units (HU). Using this threshold on a per-patient basis, the automatically computed CCTA-CAC score showed a high correlation (R =0.959; P<0.01) and ICC (R =0.8219; P<0.01) with the standard Agatston CAC score. On a per-vessel basis, the standard Agatston CAC score was also highly correlated with the CCTA-CAC score (R =0.889; P<0.01 and ICC =0.717; P<0.01). Of the 62 patients enrolled, 47 (76%) were classified into the same cardiovascular risk category using the CCTA-CAC score quantification method as when the standard Agatston CAC score was used. Agreement within the CAC categories was also good (kappa =0.7560). Fully automated quantification of the Agatston CAC score on CCTA images is feasible and shows a high correlation with the reference standard. This method could simplify the quantification procedure and has the potential to reduce the radiation dose and save time by eliminating the non-contrast cardiac CT stage.

Automatic epicardial fat segmentation and volume quantification on non-contrast cardiac Computed Tomography

Epicardial Fat Volume (EFV) represents a valuable predictor of cardio- and cerebrovascular events. However, the manual procedures for EFV calculation, diffused in clinical practice, are highly time-consuming for technicians or physicians and often involve significant intra- or inter-observer variances. To reduce the processing time and improve results repeatability, we propose a computer-assisted tool that automatically performs epicardial fat segmentation and volume quantification on non-contrast cardiac Computed Tomography (CT). The proposed algorithm prioritizes the use of basic image techniques, promoting lower computational complexity. The heart region is selected using Otsu's Method, Template Matching and Connected Component Analysis. Then, to refine the pericardium delineation, convex hull is applied. Lastly, epicardial fat is segmented by thresholding. In addition to the algorithm, an intuitive software (HARTA) was developed for clinical use, allowing human intervention for adjustments. A set of 878 non-contrast cardiac CT images was used to validate the method. Using HARTA, the average time to segment the epicardial fat on a CT was 15.5 ± 2.42 s, while manually 10 to 26 min were required. Epicardial fat segmentation was evaluated obtaining an accuracy of 98.83% and a Dice Similarity Coefficient of 0.7730. EFV automatic quantification presents Pearson and Spearman correlation coefficients of 0.9366 and 0.8773, respectively. The proposed tool presents potential to be used in clinical contexts, assisting cardiologists to achieve faster and accurate EFV, leading towards personalized diagnosis and therapy. The human intervention component can also improve the automatic results and insure the credibility of this diagnostic support system. The software hereby presented is available for public access at GitHub.

Association of Quantified Costal Cartilage Calcification and Long-Term Cumulative Blood Glucose Exposure: The Multi-Ethnic Study of Atherosclerosis.

AimsAnecdotal reports have suggested increased soft tissue calcification in individuals with long-term exposures to high blood glucose. The association of costal cartilage calcification (CCC), a reliably quantifiable marker obtainable from non-contrast cardiac computed tomography (CT) with cumulative fasting blood glucose (FBG) exposure, is unknown. In this study, we aimed to determine the association between quantified CCC and cumulative glucose exposure using non-contrast coronary artery calcium (CAC) scoring computed tomography (CT) images in the Multi-Ethnic Study of Atherosclerosis (MESA).MethodsThe volume of bilateral CCC was quantified in high-density pixels (threshold of Hounsfield Unit>180) using the CAC scoring CT images acquired in the 5th MESA exam. Prior long-term cumulative exposure to FBG was calculated by area under the FBG-time curve over ten years before the time of the CT exam.ResultsA total of 2,305 participants (mean age: 69, female/male: 1.3) were included in this study. The median CCC volume was lower in females than males (1158 mm3 [IQR: 1751] vs. 3054 mm3 [3851], p<0.001). In cross-sectional analysis, quantified CCC was associated with FBG (9% increase per SD) and HbA1c (7% increase per SD) at the CT exam only in female participants after adjustment for age, race, BMI, and glomerular filtration rate. Only in female participants, quantified CCC was also associated with prior cumulative FBG (3% increase per decile change). In the subgroup of females with zero CAC scores, the adjusted CCC was still associated with FBG (13% increase per SD) at the time of CT exam and with prior cumulative FBG exposure (4% increase per decile change) before the CT exam.ConclusionsThe CCC, a reliably quantified marker in non-contrast cardiac CT, is associated with 10-year cumulative FBG exposure only in female participants, even those with zero CAC.

Computed tomography-derived characterisation of pericoronary,epicardial and paracardial adipose tissue and its association with myocardial ischemia as assessed by computed and invasive fractional flow

Abstract Introduction Increased pericoronary adipose tissue (PCAT) computed tomography (CT) attenuation derived from coronary CT angiography (CTA) around the right coronary artery (RCA) reflects coronary inflammation and relates to cardiac mortality. Purpose We aimed to investigate the yet unclear association between CT-derived characterisation of different cardiac adipose tissue compartments and the presence of myocardial ischemia as assessed by fractional flow reserve (FFR). Methods 133 stable individuals (64 years, 74% male) with coronary artery disease (CAD) underwent CTA including computed FFR (FFR-CT) measurement followed by invasive angiography with FFR (invasive FFR) assessment. The CT attenuation (HU) and volume (mm3) of PCAT were quantified around the RCA (10 to 50 mm from RCA ostium), the proximal 40 mm of the left anterior descending artery (LAD) and the circumflex artery (LCX) with the help of semi-automated software. The per patient PCAT CT attenuation was calculated as followed: (PCAT CT attenuation of RCA+LAD+LCX)/3. Quantification of epicardial adipose tissue (EAT) and paracardial adipose tissue (PAT; all intrathoracic adipose tissue outside the pericardium) were performed in non-contrast cardiac CT data sets using a fully automated deep-learning based algorithm. Results Median FFR-CT was 0.86 [0.79, 0.91] and median invasive FFR was 0.87 [0.81, 0.93]. Patients with presence of myocardial ischemia (n=26) defined by a FFR-CT threshold of ≤0.75 showed a significant higher PCAT CT attenuation of RCA (−75.1 HU vs. −81.1 HU, p=0.011) and per patient (−74.5 HU vs. −77.7 HU, p=0.045) than individuals without myocardial ischemia (n=107). In multivariable analysis adjusted for age, BMI, gender and traditional risk factors, both RCA and per patient PCAT CT attenuation were significant predictors of myocardial ischemia as assessed by FFRCT ≤0.75. Between individuals with myocardial ischemia compared to individuals without myocardial ischemia there was no significant difference neither in the volume and CT attenuation of EAT and PAT nor in the PCAT volume of RCA, LAD, LCX and per patient PCAT volume. Conclusions Our observations suggest that PCAT CT attenuation instead of PCAT volume, EAT and PAT measures might be associated with the presence of myocardial ischemia as assessed by FFR. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): German Heart Foundation e.V.

Inter-Software and Inter-Scan Variability Amongst Post-Processing Software Platforms in Measurement of Epicardial Adipose Tissue

There is lack of standardisation of post-processing software platforms in the assessment of epicardial adipose tissue (EAT) on cardiac computed tomography (CT). This study assessed the inter-vendor agreement of EAT volume (EATv) and attenuation (EATa) on both contrast-enhanced (CE) and non-contrast CT (NCT) using freeware imaging software (OsiriX), EAT research-specific software (QFAT), and standard coronary CT reporting software (Vitrea).

Prognostic importance of left atrial size measured by non-contrast cardiac computed tomography – A DANCAVAS study

BackgroundEnlargement of left atrium (LA) is a valuable marker of cardiovascular events, and LA size is readily available while performing non-contrast cardiac computed tomography (NCCT) for preventive purposes. We aimed to evaluate the predictive value of a single LA area from NCCT in a population-based cohort. MethodMainly men aged 60–75 years from DANCAVAS were included. Traditional risk factors were recorded, and an NCCT scan performed at baseline. Coronary artery calcifications (CAC) score and the largest LA area were measured. LA was indexed to body surface area and categorised into four groups. Data on incident atrial fibrillation (AF), thromboembolic events, heart failure (HF) and death were obtained from Danish national registries. ResultsIn total, 14,557 individuals were eligible, excluding those without LA measurement (N = 232) and with heart valve replacement (N = 197). Known AF or HF were respectively excluded from follow-up. Median follow-up time was 2.1 to 3.4 years. In total, 304 developed AF, 149 had thromboembolism, 129 developed HF and 482 died. In adjusted analysis, LA enlargement was associated with AF (HR (95% CI): large 1.99 (1.46–2.71) and very large LA 3.77 (2.31–6.14)) and HF (large 2.40 (1.50–3.85) and very large LA 6.54 (4.07–10.51)). A very large LA significantly increased mortality (HR: 2.01 (1.44–2.82)), and was associated with a two-fold increased risk of thromboembolism; however, not significantly in adjusted analysis (p = 0.09). ConclusionWe demonstrated that determination of LA area from NCCT was an important predictor of AF, HF and death. This knowledge could inform current risk assessment beyond CAC score.

Artificial intelligence applied to non-contrast-enhanced cardiac computed tomography for the prediction of cardiovascular events

Abstract Background Non-contrast-enhanced cardiac computed tomography (CT) may provide two measures that are emerging as independent predictors of cardiovascular events: coronary calcium score (CCS) and the volume of epicardial fat, a metabolically and immunologically active tissue surrounding the coronary arteries. The quantification of epicardial fat volume (EFV) is not routinely performed in clinical practice for the long time required for image reconstruction and the intra- and inter-observer variability. Purpose We evaluated if artificial intelligence (AI) might prove a valuable tool to interpret the CT data-set, and to better understand the relative prognostic value of CCS and EFV compared to “traditional” cardiovascular risk factors. Methods The Montignoso HEart and Lung Project is a community-based study carried out in a small town of Northern Tuscany (Italy). Starting from 2009, asymptomatic individuals from the general population underwent a baseline screening including a non-contrast cardiac CT, and were followed-up. For the present study, CCS and EFV were automatically measured from CT scans through a deep learning (DL) strategy based on convolutional neural networks. Because of the low incidence of the primary endpoint (myocardial infarction [MI]), the observed cardiac events were predicted with a random forest model built using a subsampling approach. Results Study participants (n=1528; 48% males, age 40 to 77 years) experienced 47 MI events (3%) over 5.5±1.5 years. CCS and EFV independently predicted this endpoint (p values &amp;lt;0.001 and 0.005, respectively) in a model including other predictors, namely weight, age, male gender, and hypertension. The model displayed a good prognostic performance, with an out-of-bag accuracy of 80.43% (accuracy on non-event prediction: 81.17%; performance on event prediction: 57,45%). The CCS emerged as the most important predictor, followed by EFV, weight and age. Interestingly, the incidence of cardiovascular events linked with CCS levels was associated with elevated EFV and the subjects with elevated CCS values but low EFV had no events (figure 1). Conclusions The tools of AI allow to perform an automated analysis of non-contrast-enhanced CT scans, with rapid and accurate measurement of CCS and EFV through a DL approach. In asymptomatic individuals from the general population, these features are more predictive of non-fatal MI than other variables related to the cardiovascular risk, as we can be demonstrated through an application of AI. Figure 1 Funding Acknowledgement Type of funding source: None

The association between aortic valve calcification, cardiovascular risk factors, and cardiac size and function in a general population

To determine the presence and extent of aortic valve calcification (AVC) quantified by non-contrast cardiac computed tomography (NCCT), to determine the association between traditional cardiovascular risk factors and AVC score, and to evaluate the association between AVC and cardiac size and function assessed by echocardiography, in a general population aged 65-75years. A random sample of 2060 individuals were invited to undergo NCCT through which their AVC score was assessed. Individuals with an AVC score ≥ 300 arbitrary units (AU) were invited for a transthoracic echocardiography together with age-matched controls. Descriptive statistics and multiple regression analyses were performed to identify risk factors associated with AVC and to describe associations between AVC score and echocardiographic findings. Of 2060 individuals invited 664 males and 636 females participated. Among those, 455 (68.5%) of males and 358 (56.3%) of females had AVC scores > 0 AU. The median AVC score was 6 AU (IQR 0-3064). Seventy-seven (11.6%) males and 20 (3.1%) females had an AVC score ≥ 300 AU. In a multiple regressionanalysis, age, sex, prior cardiovascular disease, smoking, and hypertension were associated with AVC score, while diabetes, hypercholesterolemia and kidney function were not. Individuals with AVC ≥ 300 AU had higher peak and mean aortic valve gradient, smaller indexed aortic valve area, greater left ventricular mass, and larger left atrial (LA) volume. In a random population sample of individuals aged 65-75years, AVC was common and associated with most known cardiovascular risk factors. AVC ≥ 300 AU was associated with concentric remodeling and LA dilatation.

Impact of epicardial adipose tissue volume upon left ventricular dysfunction in patients with mild-to-moderate aortic stenosis: A post-hoc analysis.

BackgroundAortic stenosis (AS) may lead to diastolic dysfunction and later on heart failure (HF) with preserved left ventricular ejection fraction (HFpEF) via increased afterload and left-ventricular (LV) hypertrophy. Since epicardial adipose tissue (EAT) is a metabolically active fat depot that is adjacent to the myocardium and can influence cardiomyocytes and LV function via secretion of proinflammatory cytokines, we hypothesized that high amounts of EAT, as assessed by computed tomography (CT), may aggravate the development and severity of LV hypertrophy and diastolic dysfunction in the context of AS.MethodsWe studied 50 patients (mean age 71 ± 9 years; 9 women) in this preliminary study with mild or moderate AS and mild to severe LV diastolic dysfunction (LVDD), diagnosed by echocardiography, who underwent non-contrast cardiac CT and echocardiography. EAT parameters were measured on 2nd generation dual source CT. Conventional two-dimensional echocardiography and Tissue Doppler Imaging (TDI) was performed to assess LV function and to derive myocardial straining parameter. All patients had a preserved LV ejection fraction > 50%. Data was analysed using Pearson’s correlation.ResultsOnly weak correlation was found between EAT volume or density and E/é ratio as LVDD marker (r = -.113 p = .433 and r = .260, p = .068 respectively). Also, EAT volume or density were independent from Global Strain Parameters (r = 0.058 p = .688 and r = -0.207 p = .239). E/é ratio was strongly associated with LVDD (r = .761 p≤0.0001) and Strain Parameters were moderately associated with LV Ejection Fraction (r = -.669 p≤0.001 and r = -.454 P≤0.005).ConclusionsIn this preliminary study in patients with AS, the EAT volume and density as assessed by CT correlated only weakly with LVDD, as expressed by the commonly used E/é ratio, and with LV strain function. Hence, measuring EAT volume and density may neither contribute to the prediction nor upon the severity of LVDD, respectively.

Measures of Adipose Tissue Redistribution and Atherosclerotic Coronary Plaque in HIV.

People with HIV (PWH) who are well treated on antiretroviral therapy remain at increased risk for body composition changes, including increased visceral adipose tissue (VAT) and reduced subcutaneous adipose tissue (SAT), as well as increased cardiovascular disease (CVD). The relationship between adipose compartments and coronary disease is not well understood among PWH. A total of 148 PWH and 68 uninfected individuals without CVD were well phenotyped for VAT and SAT via single-section abdominal computed tomography (CT) at L4. Coronary artery calcium (CAC) score was assessed by noncontrast cardiac CT and coronary plaque composition by coronary CT angiography. Increased VAT was significantly related to increased presence of plaque (OR, 1.55 per 100 cm2 ; P = 0.008) and CAC > 0 (OR, 1.56 per 100 cm2 ; P = 0.006) in the HIV group. In contrast, increased SAT was related to reduced presence of plaque (OR, 0.79 per 100 cm2 ; P = 0.057) and reduced CAC > 0 (OR, 0.69 per 100 cm2 , P = 0.007) among PWH. The VAT to SAT ratio showed a strong relationship to overall presence of calcified plaque (OR, 3.30; P = 0.03) and CAC > 0 (OR, 3.57; P < 0.001) in the HIV group. VAT and waist to hip ratio, but not SAT, were strong predictors of plaque in the uninfected group. BMI did not relate in either group. Fat redistribution phenotyping by simultaneous quantification of VAT and SAT as independent measures could help identify those PWH at higher risk for CVD.

P6151Fully automated epicardial adipose tissue volume and density measured from non-contrast CT predict major adverse cardiovascular events in asymptomatic subjects

Abstract Background Epicardial adipose tissue (EAT) volume and density has shown to correlate with standard markers of coronary artery disease (CAD) and may predict major adverse cardiovascular events (MACE). Purpose We aimed to evaluate the prognostic value of EAT volume and density measured by fully automated deep-learning software from non-contrast cardiac computed tomography (CT). Methods We assessed 2071 consecutive asymptomatic subjects (age 56±9 years, 59% male) from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial with long-term follow-up after coronary artery calcium (CAC) measurement. EAT volume and mean density were quantified using automated deep-learning software from non-contrast cardiac CT. MACE was defined as myocardial infarction (MI), cardiac death, late (&gt;90 days) revascularization and acute coronary syndrome (ACS). EAT volume and density were systematically compared to CAC score and atherosclerotic cardiovascular disease (ASCVD) risk score using Cox proportional hazards regression for MACE prediction. Results At 14±3 years, 217 subjects suffered MACE. In age-and-gender-adjusted multivariate analysis, ASCVD risk score, CAC (two-fold increase) and EAT volume (two-fold increase) were associated with increased risk of suffering MACE [Hazard Ratio (HR) (95% CI): 1.03 (1.01–1.04); 1.25 (1.19–1.30); and 1.36 (1.08–1.70) respectively, p&lt;0.01 for all] (Figure); the corresponding Harrell's C-statistic was 0.76. The area-under-the curve from receiver-operator characteristic analysis for MACE prediction increased significantly from 0.69 to 0.77 (p&lt;0.0001) when EAT volume and CAC were added to the current clinical standard (ASCVD, family history and obesity measures BMI and BSA). Both in men and women, increase in EAT volume was associated with increased risk of MACE, with HR 1.14 (1.06–1.22), p&lt;0.001 in men vs. 1.15 (1.01–1.31), p=0.03 in women, for each 20 cubic centimeter increase in volume. EAT density (HU) was independently inversely associated with MACE [HR: 0.96 (0.93–0.99), p=0.01]. MACE Prediction Conclusions EAT volume and density measurements improve prediction of MACE in asymptomatic populations over the current clinical standard. Fully automated EAT volume and density quantification by deep-learning from non-contrast cardiac CT can provide additional prognostic value for the asymptomatic patient. Acknowledgement/Funding 1R01HL133616, Forschungsstiftung Medizin Universitätsklinikum Erlangen, grant from Dr Miriam and Sheldon G. Adelson Medical Research Foundation