Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome

Epicardial fat volume is associated with primary coronary slow-flow phenomenon in patients with severe aortic stenosis undergoing transcatheter valve implantation

BACKGROUND

Primary coronary slow flow (CSF) is defined as delayed opacification of the distal epicardial vasculature during coronary angiography in the absence of relevant coronary artery stenoses. Microvascular disease is thought to be the underlying cause of this pathology. Epicardial fat tissue (EFT) is an active endocrine organ directly surrounding the coronary arteries that provides pro-inflammatory factors to the adjacent tissue by paracrine and vasocrine mechanisms. The aim of the present study was to investigate a potential association between EFT and primary CSF and whether EFT can predict the presence of primary CSF.

METHODS

Between 2016 and 2017, n = 88 patients with high-grade aortic stenosis who were planned for transcatheter aortic valve implantation (TAVI) were included in this retrospective study. EFT volume was measured by pre-TAVI computed tomography (CT) using dedicated software. The presence of primary CSF was defined based on the TIMI frame count from the pre-TAVI coronary angiograms.

RESULTS

Thirty-nine of 88 TAVI patients had CSF (44.3%). EFT volume was markedly higher in patients with CSF (142 ml [IQR 107-180] vs. 113 ml [IQR 89-147]; p = 0.009) and was strongly associated with the presence of CSF (OR 1.012 [95%CI 1.002-1.021]; p = 0.014). After adjustment, EFT volume was still an independent predictor of CSF (OR 1.016 [95%CI 1.004-1.026]; p = 0.009).

CONCLUSION

Primary CSF was independently associated with increased EFT volume. Further studies are needed to validate this finding and elucidate whether a causal relationship exists.

The Role of Artificial Intelligence in Cardiac Imaging

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.

Pericardial fat volume is related to endothelial-mediated coronary blood flow in women with suspected coronary microvascular dysfunction. A report from the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD) study

Background

Coronary microvascular dysfunction is prevalent in women with signs and symptoms of ischemia but no obstructive coronary artery disease (CAD) and is associated with an adverse prognosis. Elevated pericardial fat volume predicts adverse cardiac events, but mechanistic pathways of the association are not well understood.

Methods

118 women enrolled in the NHLBI-sponsored Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction study with suspected coronary microvascular dysfunction but no obstructive CAD underwent adenosine stress 1.5 T cardiovascular magnetic resonance imaging (CMR) imaging and invasive coronary reactivity testing. Semi-quantitative myocardial perfusion reserve index (MPR) index was derived from perfusion images. Pericardial fat volume was measured by manually contouring the cardiac margins and adjacent adipose tissue on a single trans-axial HASTE slice at the level of the left main coronary artery origin and indexed to body surface-area. Simple standard deviation analysis obtained for continuous variables and frequency (percent) for categorical variables. The relationships between pericardial fat volume and coronary reactivity testing parameters were examined by correlation and multivariable regression analyses.

Results

Women with suspected coronary microvascular dysfunction had a mean age of 55 ± 10 years, body mass index (BMI) of 28 ± 7 kg/m2, 44 % had a history of smoking, 63 % hypertension, 8 % diabetes, and 20 % dyslipidemia. CMR imaging-derived pericardial fat volume and coronary blood flow response to intracoronary acetylcholine (Δ CBF) were negatively correlated (r = -0.32, p = 0.0013). After adjustment for age, number of risk factors, high-density lipoprotein (HDL), and cold pressor diameter response, pericardial fat volume remained a significant predictor of Δ coronary blood flow (p = 0.04). There was no association with other coronary reactivity testing measures or CMRI derived MPR index.

Conclusions

Among women with suspected coronary microvascular dysfunction but no obstructive CAD, pericardial fat volume appears to be related in a hypothesized adverse direction to coronary microvascular endothelial function. These results support further work confirming and extending these results to investigate pericardial fat volume as mechanistic pathway and potential treatment target for coronary microvascular dysfunction-related adverse events.Trial registration: clinicaltrials.govNCT00832702.

Pericoronary adipose tissue attenuation in patients with acute aortic dissection based on coronary computed tomography angiography

Background

Periaortic fat is associated with coronary disease. Thus, it was hypothesized that the inflammation associated with acute aortic dissection (AAD) spreads to pericoronary adipose tissue (PCAT) via thoracic periaortic fat. Pericoronary adipose tissue attenuation (PCATa) serves as a marker for inflammation of perivascular adipose tissue (PVAT). This study sought to examine PCATa in individuals diagnosed with AAD.

Methods

Consecutive patients with chest pain from May 2020 to September 2022 were prospectively enrolled in this study and underwent coronary computed tomography angiography (CCTA) and/or aorta computed tomography angiography (CTA). Based on the results of the CTA, the patients were divided into the following two groups: (I) the AAD group; and (II) the non-AAD group. PCATa of the right coronary angiography (RCA), left anterior descending (LAD), and left circumflex (LCx) was quantified for each patient using semi-automated software. The PCATa values were compared between the AAD and non-AAD patients according to the atherosclerosis of the coronary arteries. Similarly, the PCATa values of the AAD patients were compared between the preoperative and postoperative steady states.

Results

A total of 136 patients (42 female, 94 male; mean age: 63.3±11.9 years) were divided into the two groups according to the presence of aortic dissection on CTA. The RCAPCATa, LADPCATa, and LCxPCATa values were significantly higher in the AAD subjects than the non-AAD subjects, regardless of the presence or absence of atherosclerosis in the coronary arteries [-85.1±9.3 vs. -92.9±10.0 Hounsfield unit (HU); -83.2±7.4 vs. -89.9±9.1 HU; -77.5±8.4 vs. -85.6±7.9 HU, all P<0.001). The preoperative RCAPCATa, LADPCATa, and LCxPCATa values were higher in the AAD patients than the postoperative steady-state patients (-82.9±8.7 vs. -97.6±8.8 HU; -79.8±7.6 vs. -92.8±6.8 HU; -74.6±7.1 vs. -87.7±6.9 HU, all P<0.001). According to the multivariable logistic regression analysis, high RCAPCATa and LADPCATa values were associated with AAD regardless of the degree of stenosis [odds ratio (OR) =0.014; 95% confidence interval (CI): 0.001-0.177; P=0.001 and OR =0.010; 95% CI: 0.001-0.189; P=0.002].

Conclusions

PCATa on computed tomography was increased in patients with AAD regardless of the presence or absence of coronary artery disease (CAD). This suggests that vascular inflammation is present in AAD independent of CAD. Further research should be conducted to investigate the potential of this imaging biomarker to predict AAD and monitor patients' responses to therapies for AAD.

Leptin-dependent differential remodeling of visceral and pericardial adipose tissue following chronic exercise and psychosocial stress

Obesity is driven by an imbalance between caloric intake and energy expenditure, causing excessive storage of triglycerides in adipose tissue at different sites around the body. Increased visceral adipose tissue (VAT) is associated with diabetes, while pericardial adipose tissue (PAT) is associated with cardiac pathology. Adipose tissue can expand either through cellular hypertrophy or hyperplasia, with the former correlating with decreased metabolic health in obesity. The aim of this study was to determine how VAT and PAT remodel in response to obesity, stress, and exercise. Here we have used the male obese Zucker rats, which carries two recessive fa alleles that result in the development of hyperphagia with reduced energy expenditure, resulting in morbid obesity and leptin resistance. At 9 weeks of age, a group of lean (Fa/Fa or Fa/fa) Zucker rats (LZR) and obese (fa/fa) Zucker rats (OZR) were treated with unpredictable chronic mild stress or exercise for 8 weeks. To determine the phenotype for PAT and VAT, tissue cellularity and gene expression were analyzed. Finally, leptin signaling was investigated further using cultured 3T3-derived adipocytes. Tissue cellularity was determined following hematoxylin and eosin (H&E) staining, while qPCR was used to examine gene expression. PAT adipocytes were significantly smaller than those from VAT and had a more beige-like appearance in both LZR and OZR. In the OZR group, VAT adipocyte cell size increased significantly compared with LZR, while PAT showed no difference. Exercise and stress resulted in a significant reduction in VAT cellularity in OZR, while PAT showed no change. This suggests that PAT cellularity does not remodel significantly compared with VAT. These data indicate that the extracellular matrix of PAT is able to remodel more readily than in VAT. In the LZR group, exercise increased insulin receptor substrate 1 (IRS1) in PAT but was decreased in the OZR group. In VAT, exercise decreased IRS1 in LZR, while increasing it in OZR. This suggests that in obesity, VAT is more responsive to exercise and subsequently becomes less insulin resistant compared with PAT. Stress increased PPAR-γ expression in the VAT but decreased it in the PAT in the OZR group. This suggests that in obesity, stress increases adipogenesis more significantly in the VAT compared with PAT. To understand the role of leptin signaling in adipose tissue remodeling mechanistically, JAK2 autophosphorylation was inhibited using 5 μM 1,2,3,4,5,6-hexabromocyclohexane (Hex) in cultured 3T3-derived adipocytes. Palmitate treatment was used to induce cellular hypertrophy. Hex blocked adipocyte hypertrophy in response to palmitate treatment but not the increase in lipid droplet size. These data suggest that leptin signaling is necessary for adipocyte cell remodeling, and its absence induces whitening. Taken together, our data suggest that leptin signaling is necessary for adipocyte remodeling in response to obesity, exercise, and psychosocial stress.

Opportunistic screening at chest computed tomography: literature review of cardiovascular significance of incidental findings

Background and Objective

Several incidental cardiovascular findings are present in a routine chest computed tomography (CT) scan, many of which do not make it to the final radiology report. However, these findings have important clinical implications, particularly providing prognosis and risk-stratification for future cardiovascular events. The purpose of this article is to review the literature on these incidental cardiovascular findings in a routine chest CT and inform the radiologist on their clinical relevance.

Methods

A time unlimited review of PubMed and Web of Science was performed by using relevant keywords. Articles in English that involved adults were included.

Key Content and Findings

Coronary artery calcification (CAC) is the most common incidental cardiac finding detected in a routine chest CT and is a significant predictor of cardiovascular events. Noncoronary vascular calcifications in chest CT include aortic valve, mitral annulus, and thoracic aortic calcifications (TAC). Among these, aortic valve calcification (AVC) has the strongest association with coronary artery disease and cardiovascular events. Additional cardiac findings such as myocardial scar and left ventricular size and noncardiac findings such as thoracic fat, bone density, hepatic steatosis, and breast artery calcifications can also help in risk stratification and patient management.

Conclusions

The radiologist interpreting a routine chest CT should be cognizant of the incidental cardiovascular findings, which helps in the diagnosis and risk-stratification of cardiovascular disease. This will guide appropriate referral and management.

Computed tomography measured epicardial adipose tissue and psoas muscle attenuation: new biomarkers to predict major adverse cardiac events (MACE) and mortality in patients with heart disease and critically ill patients. Part I: Epicardial adipose tissue

Over the last two decades, the potential role of epicardial adipocyte tissue (EAT) as a marker for major adverse cardiovascular events has been extensively studied. Unlike other visceral adipocyte tissues (VAT), EAT is not separated from the adjacent myocardium by a fascial layer and shares the same microcirculation with the myocardium. Adipocytokines, secreted by EAT, interact directly with the myocardium through paracrine and vasocrine pathways. The role of the Randle cycle, linking VAT accumulation to insulin resistance, and the relevance of blood flow and mitochondrial function of VAT, are briefly discussed. The three available imaging modalities for the assessment of EAT are discussed. The advantages of echocardiography, cardiac CT, and cardiac magnetic resonance (CMR) are compared. The last section summarises the current stage of knowledge on EAT as a clinical marker for major adverse cardiovascular events (MACE). The association between EAT volume and coronary artery disease (CAD) has robustly been validated. There is growing evidence that EAT volume is associated with computed tomography coronary angiography (CTCA) assessed high-risk plaque features. The EAT CT attenuation coefficient predicts coronary events. Many studies have established EAT volume as a predictor of atrial fibrillation after cardiac surgery. Moreover, EAT thickness has been independently associated with severe aortic stenosis and mitral annular calcification. Studies have demonstrated that EAT volume is associated with heart failure. Finally, we discuss the potential role of EAT in critically ill patients admitted to the intensive care unit. In conclusion, EAT seems to be a promising new biomarker to predict MACE.

Epicardial fat volume is associated with preexisting atrioventricular conduction abnormalities and increased pacemaker implantation rate in patients undergoing transcatheter aortic valve implantation

Epicardial fat tissue (EFT) is a highly metabolically active fat depot surrounding the heart and coronary arteries that is related to early atherosclerosis and adverse cardiac events. We aimed to investigate the relationship between the amount of EFT and preexisting cardiac conduction abnormalities (CCAs) and the need for new postinterventional pacemaker in patients with severe aortic stenosis planned for transcatheter aortic valve implantation (TAVI). A total of 560 consecutive patients (54% female) scheduled for TAVI were included in this retrospective study. EFT volume was measured via a fully automated artificial intelligence software (QFAT) using computed tomography (CT) performed before TAVI. Baseline CCAs [first-degree atrioventricular (AV) block, right bundle branch block (RBBB), and left bundle branch block (LBBB)] were diagnosed according to 12-lead ECG before TAVI. Aortic valve calcification was determined by the Agatston score assessed in the pre-TAVI CT. The median EFT volume was 129.5 ml [IQR 94-170]. Baseline first-degree AV block was present in 17%, RBBB in 10.4%, and LBBB in 10.2% of the overall cohort. In adjusted logistic regression analysis, higher EFT volume was associated with first-degree AV block (OR 1.006 [95% CI 1.002-1.010]; p = 0.006) and the need for new pacemaker implantation after TAVI (OR 1.005 [95% CI 1.0-1.01]; p = 0.035) but not with the presence of RBBB or LBBB. EFT volume did not correlate with the Agatston score of the aortic valve. Greater EFT volume is associated independently with preexisting first-degree AV block and new pacemaker implantation in patients undergoing TAVI. It may play a causative role in degenerative processes and the susceptibility of the AV conduction system.

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This monocentric retrospective study included 353 patients: 95 for training, 20 for testing, and 238 for prognosis evaluation. EAT segmentation was obtained after thresholding on a manually segmented pericardial volume. The model was evaluated with Dice coefficient (DSC), inter-and intraobserver reproducibility, and clinical measures. Uni-and multi-variate analyzes were conducted to assess the prognosis value of the EAT volume, EAT extent, and lung lesion extent on clinical outcomes, including hospitalization, oxygen therapy, intensive care unit admission and death. Results: The mean DSC for EAT volumes was 0.85 ± 0.05. For EAT volume, the mean absolute error was 11.7 ± 8.1 cm3 with a non-significant bias of −4.0 ± 13.9 cm3 and a correlation of 0.963 with the manual measures (p < 0.01). The multivariate model providing the higher AUC to predict adverse outcome include both EAT extent and lung lesion extent (AUC = 0.805). Conclusions: A DL algorithm was developed and evaluated to obtain reproducible and precise EAT segmentation on LDCT. EAT extent in association with lung lesion extent was associated with adverse clinical outcomes with an AUC = 0.805.

Development of a Nomogram for Estimating the Risk of Left Ventricular Diastolic Dysfunction in Patients with Non-Alcoholic Fatty Liver Disease

BACKGROUND

Patients with non-alcoholic fatty liver disease (NAFLD) are more likely to develop left ventricular diastolic dysfunction (LVDD). Although lifestyle adjustments contribute to the improvement of NAFLD, thereby delaying or even preventing LVDD progression, it is difficult to maintain a healthy lifestyle, resulting in a higher incidence of LVDD in NAFLD patients.

OBJECTIVE

This study aims to develop a nomogram for assessing the risk of LVDD progression in NAFLD patients to increase their adherence to therapeutic interventions and adjust their treatment regimens timely.

METHODS

A total of 148 medical records of NAFLD patients were retrospectively analyzed. Sixty-three were assigned to the LVDD+ group and 85 were assigned to the LVDD- group. The independent correlates of LVDD, which were screened via least absolute shrinkage and selection operator logistic regression model first, followed by multivariate Logistic regression model, constituted the nomogram to determine the likelihood of LVDD in NAFLD patients.

RESULTS

Number of comorbidities, glycosylated hemoglobin, and epicardial adipose tissue (EAT) volume index were independent correlates of LVDD (all P < 0.05). They served as components in the newly developed nomogram. It obtained significant clinical benefit in detecting NAFLD patients at the risk of LVDD progression, with satisfied discrimination and calibration.

CONCLUSION

We developed a nomogram for identifying NAFLD patients with a normal diastolic function who are at risk of LVDD progression, thus contributing to effective prevention of LVDD progression.

Association between single-slice and whole heart measurements of epicardial and pericardial fat in cardiac MRI

BACKGROUND

Epicardial (ECF) and pericardial fat (PCF) are important prognostic markers for various cardiac diseases. However, volumetry of the fat compartments is time-consuming.

PURPOSE

To investigate whether total volume of ECF and PCF can be estimated by axial single-slice measurements and in a four-chamber view.

MATERIAL AND METHODS

A total of 113 individuals (79 patients and 34 healthy) were included in this retrospective magnetic resonance imaging (MRI) study. The total volume of ECF and PCF was determined using a 3D-Dixon sequence. Additionally, the area of ECF and PCF was obtained in single axial layers at five anatomical landmarks (left coronary artery, right coronary artery, right pulmonary artery, mitral valve, coronary sinus) of the Dixon sequence and in a four-chamber view of a standard cine sequence. Pearson's correlation coefficient was calculated between the total volume and each single-slice measurement.

RESULTS

Axial single-slice measurements of ECF and PCF correlated strongly with the total fat volumes at all landmarks (ECF: r  =  0.85-0.94, P < 0.001; PCF: r  =  0.89-0.94, P < 0.001). The best correlation was found at the level of the left coronary artery for ECF and PCF (r  =  0.94, P < 0.001). Correlation between single-slice measurement in the four-chamber view and the total ECF and PCF volume was lower (r  =  0.75 and r  =  0.8, respectively, P < 0.001).

CONCLUSION

Single-slice measurements allow an estimation of ECF and PCF volume. This time-efficient analysis allows studies of larger patient cohorts and the opportunistic determination of ECF/PCF from routine examinations.

Artificial intelligence in cardiovascular CT: Current status and future implications

Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.

Myocardial injury in COVID-19 patients is associated with the thickness of epicardial adipose tissue

Aim    High sensitive troponin (hs-TnI) levels may increase secondary to Coronavirus disease-2019 (COVID-19), and this increase is associated with cardiovascular mortality in COVID-19 patients. Epicardial adipose tissue (EAT) is associated with myocardial injury directly as a reservoir tissue for coronavirus, and indirectly through mediators it secretes as an apocrine gland. We aimed to evaluate the relationship between myocardial injury secondary to COVID-19 infection and EAT thickness.Material and methods    Thoracic computed tomography (CT) was performed in 73 consecutive patients diagnosed with COVID-19. EAT thickness and volume were calculated by two radiologists blind to the study data. We formed two groups according to hs-TnI concentrations, patients with myocardial damage (hs-TnI ≥11.6 ng / l) and without myocardial damage (hs-TnI&lt;11.6 ng / dl).Results    A total of 46 patients were women (63.0 %). The mean age was 66.4±12.3 yrs in the myocardial injury group and 55.9±9.7 yrs in the group without myocardial injury (p&lt;0.001). There were 20 hypertensive patients (68.9 %) in the injury group, while there were 12 hypertensive patients (27.3 %) in the group without injury (p=0.001). Glucose, C-reactive protein, D-dimer, white blood cell count, neutrophil, and neutrophil / lymphocyte ratio were higher in the injury group (p&lt;0.05, for all variables). The mean EAT thickness was 5.6±1.6 mm in the injury group, whereas it was 4.8±1.8 mm in the group without injury (p=0.031). EAT thickness of 4.85 mm and above was associated with the myocardial injury with 65 % sensitivity and 39 % specificity (AUC=0.65, 95 % CI: 0.52-078, p=0.031).Conclusion    In patients with COVID-19 infection, higher rates of myocardial injury were observed as the EAT thickness increased. Epicardial adipose tissue, contributes to cytokine-mediated myocardial injury either directly or indirectly by acting as a reservoir for coronavirus. Increased EAT thickness is associated with myocardial injury in COVID-19 patients.

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Background

Artificial intelligence (AI) technology has been increasingly developed and studied in cardiac imaging. This systematic review summarizes the latest progress of image segmentation, quantification, and the clinical application of AI in evaluating cardiac adipose tissue.

Methods

We exhaustively searched PubMed and the Web of Science for publications prior to 30 April 2021. The search included eligible studies that used AI for image analysis of epicardial adipose tissue (EAT) or pericoronary adipose tissue (PCAT). The risk of bias and concerns regarding applicability were assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

Results

Of the 140 initially identified citation records, 19 high-quality studies were eligible for this systematic review, including 15 (79%) on the image segmentation and quantification of EAT or PCAT and 4 (21%) on the clinical application of EAT or PCAT in cardiovascular diseases. All 19 included studies were rated as low risk of bias in terms of flow and timing, reference standards, and the index test and as having low concern of applicability in terms of reference standards and patient selection, but 16 (84%) studies did not conduct external validation.

Conclusion

AI technology can provide accurate and quicker methods to segment and quantify EAT and PCAT images and shows potential value in the diagnosis and risk prediction of cardiovascular diseases. AI is expected to expand the value of cardiac adipose tissue imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41824-021-00107-0.

Automatic quantification of epicardial adipose tissue volume

PURPOSE

Epicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans.

METHODS

A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi-slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from -175 Hounsfield units (HU) to -15 HU for the segmentation of EAT.

RESULTS

The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1%  ±  0.7% and 96.9%  ±  0.6%, respectively. The inter-observer variability was also assessed, resulting in a Dice index of 97.0%  ±  0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4%  ±  1.5% and 93.3%  ±  1.3%, respectively, and the same measurement between the experts themselves was 93.6%  ±  1.9%. The Pearson's correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99.

CONCLUSIONS

This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg.

Alternative sites of echocardiographic epicardial fat assessment and coronary artery disease

AIMS

Increasing evidence points towards the use of epicardial fat (EF) as a reliable biomarker of coronary artery disease extent and severity. We aim to assess the different locations of echocardiographic EF thickness measurement and their relation with the presence, extent, and severity of coronary artery disease (CAD) in patients admitted with acute coronary syndromes (ACS).

METHODS

Prospective cohort study including patients admitted for ACS. EF was assessed by transthoracic echocardiography and compared with coronary angiography findings. Spearmen correlation analysis was used to search for EF correlations. Receiver-operating characteristic curve analysis was performed to assess the predictive value of the different sites of measurement of EF thickness for the presence of CAD. To evaluate other potential variables independently associated with CAD, we performed multivariate analysis employing logistic regression.

RESULTS

196 patients were included. Significant CAD was diagnosed in 83.7% of patients. In all views, EF thickness was greater in patients with CAD (p < 0.001). We found a moderate correlation between EF thickness and CAD extent and severity. EF thickness measured at RV basal level showed a good performance in predicting significant CAD in patients with ACS (AUC = 0.885, 95% CI 0.80-0.97, p < 0.001). For a value of mean RV basal region EF thickness ≥ 12.57 mm, sensitivity was 85% and specificity was 80.8%.

CONCLUSION

In patients admitted with ACS, echocardiographic EF thickness predicted the presence of CAD, as well as its extent and severity. We found EF thickness measured at the RV basal region to be the best predictor of significant CAD.

Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease

Artificial intelligence (AI) describes the use of computational techniques to perform tasks that normally require human cognition. Machine learning and deep learning are subfields of AI that are increasingly being applied to cardiovascular imaging for risk stratification. Deep learning algorithms can accurately quantify prognostic biomarkers from image data. Additionally, conventional or AI-based imaging parameters can be combined with clinical data using machine learning models for individualized risk prediction. The aim of this review is to provide a comprehensive review of state-of-the-art AI applications across various noninvasive imaging modalities (coronary artery calcium scoring CT, coronary CT angiography, and nuclear myocardial perfusion imaging) for the quantification of cardiovascular risk in coronary artery disease. © RSNA, 2021.

Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study

Background

We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals.

Methods

This was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm³) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (< 30 s per case). NAFLD was defined as liver-to-spleen attenuation ratio < 1.0 and/or average liver attenuation < 40 HU.

Results

In the final population of 2068 participants (59% males, 56 ± 9 years), those with MetS (n = 280;13.5%) had a greater prevalence of NAFLD (26.0% vs. 9.9%), higher EAT volume (114.1 cm³ vs. 73.7 cm³), and lower EAT attenuation (−76.9 HU vs. −73.4 HU; all p < 0.001) compared to those without MetS. At 14 ± 3 years, MACE occurred in 223 (10.8%) participants. In multivariable Cox regression, MetS was associated with increased risk of MACE (HR 1.58 [95% CI 1.10–2.27], p = 0.01) independently of CAC score; however, not after adjustment for EAT measures (p = 0.27). In a separate Cox analysis, NAFLD predicted MACE (HR 1.78 [95% CI 1.21–2.61], p = 0.003) independently of MetS, CAC score, and EAT measures. Addition of EAT volume to current risk assessment tools resulted in significant net reclassification improvement for MACE (22% over ASCVD risk score; 17% over ASCVD risk score plus CAC score).

Conclusions

MetS, NAFLD, and artificial intelligence-based EAT measures predict long-term MACE risk in asymptomatic individuals. Imaging biomarkers of cardiometabolic disease have the potential for integration into routine reporting of CAC scoring CT to enhance cardiovascular risk stratification.

Trial registration NCT00927693.

Association of pericardial adipose tissue with left ventricular structure and function: a region-specific effect?

BACKGROUND

The independent role of pericardial adipose tissue (PAT) as an ectopic fat associated with cardiovascular disease (CVD) remains controversial. This study aimed to determine whether PAT is associated with left ventricular (LV) structure and function independent of other markers of general obesity.

METHODS

We studied 2471 participants (50.9 % women) without known CVD from the Korean Genome Epidemiology Study, who underwent 2D-echocardiography with tissue Doppler imaging (TDI) and computed tomography measurement for PAT.

RESULTS

Study participants with more PAT were more likely to be men and had higher cardiometabolic indices, including blood pressure, glucose, and cholesterol levels (all P < 0.001). Greater pericardial fat levels across quartiles of PAT were associated with increased LV mass index and left atrial volume index (all P < 0.001) and decreased systolic (P = 0.015) and early diastolic (P < 0.001) TDI velocities, except for LV ejection fraction. These associations remained after a multivariable-adjusted model for traditional CV risk factors and persisted even after additional adjustment for general adiposity measures, such as waist circumference and body mass index. PAT was also the only obesity index independently associated with systolic TDI velocity (P < 0.001).

CONCLUSIONS

PAT was associated with subclinical LV structural and functional deterioration, and these associations were independent of and stronger than with general and abdominal obesity measures.

Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans

Heart mediastinal and epicardial fat tissues are related to several adverse metabolic effects and cardiovascular risk factors, especially coronary artery disease (CAD). The manual segmentation of those fats is that the high dependence on user intervention and time-consuming analyzes. As a result, the automated measurement of cardiac fats could be considered as one of the most important biomarkers for cardiovascular risks in imaging and medical visualization by physicians. In this paper, we validate an automatic approach for the cardiac fat segmentation in non-contrast CT images then investigate the correlation between cardiac fat volume and CAD using the association rule mining algorithm. The pre-processing step includes threshold and contrast enhancement, the feature extraction step includes Gabor filter bank based on GLCM, the cardiac fat segmentation step is predicated on pattern recognition classification algorithms, and eventually, the step of investigating the relationship between cardiac fat volume and CAD is using FP-Growth algorithm. Experimental validation using CT images of two databases points to a good performance in cardiac fat segmentation. Experiments showed that the accuracy of the designed algorithm using the ensemble classifier with the best performance over other classifiers for the cardiac fat segmentation was 99.2%, with a sensitivity of 96.3% and a specificity of 99.8%. The results of using the FP-Growth algorithm showed that the low volume of epicardial (Confidence = 0.6818, Lift = 1.0626) and mediastinal (Confidence = 0.6696, Lift = 1.0436) fat are associated with healthy individuals and the high volume of epicardial (Confidence = 0.8, Lift = 2.2326) and mediastinal (Confidence = 0.75, Lift = 2.093) fat are related to individuals of CAD. As a result, cardiac fats can be used as a reliable biomarker tool in predicting the extent of CAD stenosis.

Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography

Epicardial adipose tissue (EAT) is a visceral fat deposit, that's known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent risk marker intricate. However, EAT has a variable distribution, and various diseases may affect the volume of the EAT, which can increase the complexity of the already time-consuming manual segmentation work. We propose a 3D deep attention U-Net method to automatically segment the EAT from coronary computed tomography angiography (CCTA). Five-fold cross-validation and hold-out experiments were used to evaluate the proposed method through a retrospective investigation of 200 patients. The automatically segmented EAT volume was compared with physician-approved clinical contours. Quantitative metrics used were the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). For cross-validation, the median DSC, sensitivity, and specificity were 92.7%, 91.1%, and 95.1%, respectively, with JAC, HD, CMD, MSD, and RMSD are 82.9% ± 8.8%, 3.77 ± 1.86 mm, 1.98 ± 1.50 mm, 0.37 ± 0.24 mm, and 0.65 ± 0.37 mm, respectively. For the hold-out test, the accuracy of the proposed method remained high. We developed a novel deep learning-based approach for the automated segmentation of the EAT on CCTA images. We demonstrated the high accuracy of the proposed learning-based segmentation method through comparison with ground truth contour of 200 clinical patient cases using 8 quantitative metrics, Pearson correlation, and Bland-Altman analysis. Our automatic EAT segmentation results show the potential of the proposed method to be used in computer-aided diagnosis of coronary artery diseases (CADs) in clinical settings.

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

BACKGROUND

Epicardial adipose tissue (EAT) volume (cm³) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography.

METHODS

Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (>180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction.

RESULTS

At 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01-1.04]; 1.25 [1.19-1.30]; and 1.35 [1.07-1.68], P<0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72-0.96]; P=0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm³ and coronary artery calcium ≥100 AU and was highest in subjects with both (P<0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers.

CONCLUSIONS

Fully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction.

Association between thoracic fat measured using computed tomography and lung function in a population without respiratory diseases

Background

Local fat distribution patterns and their local or systemic effects have recently attracted significant attention. The aim of this study was to assess the impact of thoracic adiposity on lung function in a population without respiratory diseases according to sex.

Methods

A total of 455 subjects (282 males and 173 females), who had undergone spirometry, and chest and abdominal computed tomography between June 2012 and June 2016 at medical healthcare center, were included. Pericardial fat, intrathoracic fat, subcutaneous thoracic fat, and both visceral and subcutaneous abdominal fat were measured by directly assessing tissue volume using computed tomography. Multiple linear regression analyses adjusted for pack-years of smoking, high-density lipoprotein, and high-sensitivity C-reactive protein were performed to evaluate the association between fat volumes and lung function.

Results

In males, intrathoracic fat and visceral abdominal fat were inversely associated with forced expiratory volume in 1 s (FEV₁) % predicted (P=0.025, P=0.010, respectively), and subcutaneous thoracic fat volumes showed a negative correlation with both FEV₁% and forced vital capacity (FVC) % predicted (P=0.019, P=0.045, respectively). In females, subcutaneous thoracic fat demonstrated a negative correlation with both FEV₁% and FVC % predicted (P=0.031 and P=0.008, respectively).

Conclusions

The influence of local thoracic fat distribution on lung function differed according to sex. Visceral fat and subcutaneous thoracic fat in males and subcutaneous fat in females were significantly associated with decreased lung function.

Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study

Purpose

To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data.

Materials and Methods

In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non-contrast material-enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans.

Results

Automated quantification was performed in a mean (± standard deviation) time of 1.57 seconds ± 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P < .001), with no significant bias (0.53 cm³; P = .13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P < .001) but with a bias of 4.35 cm³ (P < .001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P < .001), with significant bias for reader 1 (5.11 cm³; P < .001) but not for reader 2 (0.88 cm³; P = .26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P < .001) in 70 patients, with no significant bias (0.64 cm³; P = .43), and was related to an increased noncalcified plaque burden quantified from coronary CT angiography (5.7% vs 1.8%; P = .026).

Conclusion

Deep learning allows rapid, robust, and fully automated quantification of EAT from calcium scoring CT. It performs as well as an expert reader and can be implemented for routine cardiovascular risk assessment.© RSNA, 2019See also the commentary by Schoepf and Abadia in this issue.

A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans

Many studies have shown that epicardial fat is associated with a higher risk of heart diseases. Accurate epicardial adipose tissue quantification is still an open research issue. Considering that manual approaches are generally user-dependent and time-consuming, computer-assisted tools can considerably improve the result repeatability as well as reduce the time required for performing an accurate segmentation. Unfortunately, fully automatic strategies might not always identify the Region of Interest (ROI) correctly. Moreover, they could require user interaction for handling unexpected events. This paper proposes a semi-automatic method for Epicardial Fat Volume (EFV) segmentation and quantification. Unlike supervised Machine Learning approaches, the method does not require any initial training or modeling phase to set up the system. As a further key novelty, the method also yields a subdivision into quartiles of the adipose tissue density. Quartile-based analysis conveys information about fat densities distribution, enabling an in-depth study towards a possible correlation between fat amounts, fat distribution, and heart diseases. Experimental tests were performed on 50 Calcium Score (CaSc) series and 95 Coronary Computed Tomography Angiography (CorCTA) series. Area-based and distance-based metrics were used to evaluate the segmentation accuracy, by obtaining Dice Similarity Coefficient (DSC) = 93.74% and Mean Absolute Distance (MAD) = 2.18 for CaSc, as well as DSC = 92.48% and MAD = 2.87 for CorCTA. Moreover, the Pearson and Spearman coefficients were computed for quantifying the correlation between the ground-truth EFV and the corresponding automated measurement, by obtaining 0.9591 and 0.9490 for CaSc, and 0.9513 and 0.9319 for CorCTA, respectively. In conclusion, the proposed EFV quantification and analysis method represents a clinically useable tool assisting the cardiologist to gain insights into a specific clinical scenario and leading towards personalized diagnosis and therapy.

Computed Tomography Scan Overestimates the Size of Pericardial Effusion Compared to Echocardiography

Objective: Pericardial effusion is not an uncommon finding in hospitalized patients. Many pericardial effusions are found incidentally through computed tomography (CT) performed for other indications. Echocardiography is usually ordered when an incidentally discovered pericardial effusion is found on the CT to examine the effect of the effusion on hemodynamics and to detect early signs of tamponade. However, in clinical practice, the discrepancy between CT and echocardiography regarding the size of pericardial effusions is common. The accuracy of CT in the evaluation of the size of pericardial effusions is not well-studied. Our study aims to evaluate the accuracy of CT in assessing the size of a pericardial effusion compared with the gold standard echocardiography.

Methods: This is a retrospective study examining patients presenting to the University of Toledo Medical Center (UTMC) with pericardial effusions. One hundred and forty-one patient charts were reviewed and 45 subjects were excluded. Ninety-six patients in whom both CT and echocardiography were performed were enrolled in the final analysis. The time interval between both imaging modalities was limited to less than 14 days and no interventions on the effusion (e.g., pericardiocentesis) occurred in the time interval between the two imaging modalities.

Results: The size of the pericardial effusion was assessed similarly between CT and echocardiography in 50% of the cases (48/96). In the other half of the study population, the results were discrepant; CT was found to overestimate the size of pericardial effusion in 44% of the cases (42/96). The agreement rate between the two modalities is significantly low kappa = 0.111, P = 0.028.

The independent variables age, gender, body mass index (BMI), use of anticoagulants, and renal function had no effect on the agreement between CT and echocardiography.

Conclusion: Computerized tomography tends to overestimate the size of the pericardial effusion compared to echocardiography. Based on an incidental finding of pericardial effusion on CT scan, this discrepancy should be recognized prior to ordering an echocardiogram. Echocardiography can be considered in relevant clinical settings.

Epicardial adipose tissue thickness in systemic sclerosis patients without overt cardiac disease

Systemic sclerosis is associated with an increased prevalence/incidence of coronary artery disease. The aim of this study was to investigate epicardial adipose tissue (EAT) thickness which may contribute to cardio-metabolic risk in systemic sclerosis (SSc) patients without overt cardiac disease. EAT thickness was measured by transthoracic conventional Doppler echocardiography and compared in SSc patients (n = 47) and age- and sex-matched healthy controls (n = 36). The relationships between EAT thickness and markers of cardio-metabolic risk in SSc were examined. EAT thickness was significantly greater in patients with SSc compared to healthy controls (6 [7-5] vs 5 [6.75-3.25], p = 0.041). Compared to controls, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), leukocyte, neutrophil, B-type natriuretic protein (BNP), fasting plasma insulin and HOMA-IR were elevated (18 [31-10] vs 8.5 [18-4], p < 0.001; 0.4 [0.67-0.18] vs 0.21 [0.48-0.09], p = 0.012; 7510 [8731-5990] vs 6435 [7360-5195], p = 0.002; 4350 [5440-3570] vs 3390 [4168-2903], p < 0.001; 111 [185-74] vs 70 [127-70], p = 0.010; 6.7 [10.5-4.7] vs 4.7 [6.8-4.1], p = 0.008; 1.7 [2.6-1] vs 1.1 [1.7-0.9], p = 0.015, respectively). The total and low-density lipoprotein (LDL)-cholesterol were decreased in SSc patients (197 ± 45 vs 284 ± 36, p = 0.005; 118 [148-84] vs 140 [180-115], p = 0.003, respectively). In patients with SSc, the EAT thickness correlated positively with age, ESR, CRP, insulin, hemoglobin A1c and total and LDL-cholesterol (r = 0.574, p < 0.001; r = 0.352, p = 0.015; r = 0.334, p = 0.022; r = 0.290, p = 0.048; r = 0.317, p = 0.030; r = 0.396, p = 0.006 and r = 0.349, p = 0.016, respectively). Our study confirms that EAT thickness is greater in SSc patients compared to healthy controls using echocardiographic measurements. The results of our study suggest that EAT thickness is a candidate for atherosclerotic risk assessment in SSc.

Quantification of intrathoracic fat adds prognostic value in women undergoing myocardial perfusion imaging

AIM

Amongst patients with coronary artery disease (CAD), women experience relatively worse outcomes as compared to men. Evidence to date has failed to explore unique female imaging targets as major determinants of cardiovascular risk. We sought to assess the prognostic value of epicardial (EFV) and intrathoracic fat volume (IFV) quantification in women and men with suspected and known CAD.

METHODS AND RESULTS

Intrathoracic fat volume and EFV were calculated from non-contrast CT and analyzed in a propensity-matched cohort of 190 patients (95 women, mean age 62.5 ± 11.3 years) undergoing myocardial perfusion imaging (MPI) and coronary computed tomography angiography (CCTA) for evaluation of CAD. IFV and EFV were significantly lower in women as compared to men (198.2 ± 78.4 vs 293.2 ± 114.7 cm³ and 105.6 ± 48.9 vs 135.8 ± 60.9 cm³, p < 0.001) and showed a strong association with coronary artery calcium score (CACS) and obstructive CAD in women (p < 0.05), but not in men. Fat volumes were not related to abnormal MPI in either population (p = NS). During a median follow-up of 2.8 years, high IFV was associated with reduced event free survival (log rank = 0.019 vs low IFV) in women, but not in men. Accordingly, a multivariate Cox regression model adjusted for cardiovascular risk factors, CACS, CCTA, and MPI findings selected IFV as a significant predictor of major adverse cardiovascular events (MACE) in women (HR 1.32, 95%CI 1.18-1.55, p = 0.001).

CONCLUSION

Quantification of IFV provides incremental prognostic value for MACE in women, beyond that provided by traditional risk factors and imaging findings.

Quantification of epicardial fat with cardiac CT angiography and association with cardiovascular risk factors in symptomatic patients: from the ALTER-BIO (Alternative Cardiovascular Bio-Imaging markers) registry

PURPOSE

We aimed to assess the association between features of epicardial adipose tissue and demographic, morphometric and clinical data, in a large population of symptomatic patients with clinical indication to cardiac computed tomography (CT) angiography.

METHODS

Epicardial fat volume (EFV) and adipose CT density of 1379 patients undergoing cardiac CT angiography (918 men, 66.6%; age range, 18-93 years; median age, 64 years) were semi-automatically quantified. Clinical variables were compared between diabetic and nondiabetic patients to assess potential differences in EFV and adipose CT density. Multiple regression models were calculated to find the clinical variables with a significant association with EFV and adipose CT density.

RESULTS

The median EFV in diabetic patients (112.87 mL) was higher compared with nondiabetic patients (82.62 mL; P < 0.001). The explanatory model of the multivariable analysis showed the strongest associations between EFV and BMI (β=0.442) and age (β=0.365). Significant yet minor association was found with sex (β=0.203), arterial hypertension (β=0.072), active smoking (β=0.068), diabetes (β=0.068), hypercholesterolemia (β=0.046) and cardiac height (β=0.118). The mean density of epicardial adipose tissue was associated with BMI (β=0.384), age (β=0.105), smoking (β=0.088), and diabetes (β=0.085).

CONCLUSION

In a large population of symptomatic patients, EFV is higher in diabetic patients compared with nondiabetic patients. Clinical variables are associated with quantitative features of epicardial fat.

Associations between epicardial adipose tissue, subclinical atherosclerosis and high-density lipoprotein composition in type 1 diabetes

Background

The pathophysiology of cardiovascular complications in people with type 1 diabetes (T1DM) remains unclear. An increase in epicardial adipose tissue (EAT) and alterations in the composition of high-density lipoprotein (HDL) are associated with coronary artery disease, but information on its relationship in T1DM is very limited. Our aim was to determine the association between EAT volume, subclinical atherosclerosis, and HDL composition in type 1 diabetes.

Methods

Seventy-two long-term patients with T1DM without clinical atherosclerosis were analyzed. EAT volume and subclinical atherosclerosis were measured using cardiac computed tomography angiography. EAT was adjusted according to body surface to obtain an EAT index (iEAT). HDL composition was determined.

Results

The mean iEAT was 40.47 ± 22.18 cc/m². The bivariate analysis showed positive associations of the iEAT with gender, age, hypertension, dyslipidemia, smoking, body mass index, waist circumference, insulin dose, and triglyceride (P < 0.05). The iEAT correlated positively with small HDL, increased content of apolipoprotein (apo)A-II and apoC-III, and decreased content of apoE and free cholesterol. Multiple linear regression showed that age, apoA-II content in HDL, and waist circumference were independently associated with the iEAT. Fifty percent of the patients presented subclinical atherosclerotic lesions. These patients had a higher iEAT, and their HDL contained less cholesterol and more apoA-II and lipoprotein-associated phospholipase A2 than patients without subclinical atherosclerosis.

Conclusion

Alterations in the composition of HDL in TIDM are associated with increased iEAT and the presence of subclinical atherosclerosis. We propose that these abnormalities of HDL composition could be useful to identify T1DM patients at highest cardiovascular risk.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0794-9) contains supplementary material, which is available to authorized users.

Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT

Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a new fully automated deep learning framework for EAT and thoracic adipose tissue (TAT) quantification from non-contrast coronary artery calcium computed tomography (CT) scans. The first multi-task convolutional neural network (ConvNet) is used to determine heart limits and perform segmentation of heart and adipose tissues. The second ConvNet, combined with a statistical shape model, allows for pericardium detection. EAT and TAT segmentations are then obtained from outputs of both ConvNets. We evaluate the performance of the method on CT data sets from 250 asymptomatic individuals. Strong agreement between automatic and expert manual quantification is obtained for both EAT and TAT with median Dice score coefficients of 0.823 (inter-quartile range (IQR): 0.779-0.860) and 0.905 (IQR: 0.862-0.928), respectively; with excellent correlations of 0.924 and 0.945 for EAT and TAT volumes. Computations are performed in <6 s on a standard personal computer for one CT scan. Therefore, the proposed method represents a tool for rapid fully automated quantification of adipose tissue and may improve cardiovascular risk stratification in patients referred for routine CT calcium scans.

Comparison of epicardial adipose tissue radiodensity threshold between contrast and non-contrast enhanced computed tomography scans: A cohort study of derivation and validation

BACKGROUND AND AIMS

Epicardial adipose tissue (EAT) volume derived from contrast enhanced (CE) computed tomography (CT) scans is not well validated. We aim to establish a reliable threshold to accurately quantify EAT volume from CE datasets.

METHODS

We analyzed EAT volume on paired non-contrast (NC) and CE datasets from 25 patients to derive appropriate Hounsfield (HU) cutpoints to equalize two EAT volume estimates. The gold standard threshold (-190HU, -30HU) was used to assess EAT volume on NC datasets. For CE datasets, EAT volumes were estimated using three previously reported thresholds: (-190HU, -30HU), (-190HU, -15HU), (-175HU, -15HU) and were analyzed by a semi-automated 3D Fat analysis software. Subsequently, we applied a threshold correction to (-190HU, -30HU) based on mean differences in radiodensity between NC and CE images (ΔEATrd = CE radiodensity - NC radiodensity). We then validated our findings on EAT threshold in 21 additional patients with paired CT datasets.

RESULTS

EAT volume from CE datasets using previously published thresholds consistently underestimated EAT volume from NC dataset standard by a magnitude of 8.2%-19.1%. Using our corrected threshold (-190HU, -3HU) in CE datasets yielded statistically identical EAT volume to NC EAT volume in the validation cohort (186.1 ± 80.3 vs. 185.5 ± 80.1 cm³, Δ = 0.6 cm³, 0.3%, p = 0.374).

CONCLUSIONS

Estimating EAT volume from contrast enhanced CT scans using a corrected threshold of -190HU, -3HU provided excellent agreement with EAT volume from non-contrast CT scans using a standard threshold of -190HU, -30HU.

Indexed Left Atrial Adipose Tissue Area Is Associated With Severity of Atrial Fibrillation and Atrial Fibrillation Recurrence Among Patients Undergoing Catheter Ablation

Background: Epicardial adipose tissue (EAT) has been associated with adverse left atrial (LA) remodeling and atrial fibrillation (AF) outcomes, possibly because of paracrine signaling. Objectives: We examined factors associated with a novel measure of EAT i.e., indexed LAEAT (iLAEAT) and its prognostic significance after catheter ablation (CA) of atrial fibrillation (AF). Methods: We performed a retrospective analysis of 274 participants with AF referred for CA. LAEAT area was measured from a single pre-ablation CT image and indexed to body surface area (BSA) to calculate iLAEAT. Clinical, echocardiographic data and 1-year AF recurrence rates after CA were compared across tertiles of iLAEAT. We performed logistic regression analysis adjusting for factors associated with AF to examine relations between iLAEAT and AF recurrence. Results: Mean age of participants was 61 ± 10 years, 136 (49%) were women, mean BMI was 32 ± 9 kg/m2 and 85 (31%) had persistent AF. Mean iLAEAT was 0.82 ± 0.53 cm2/m2. Over 12-months, 109 (40%) had AF recurrence. Participants in the highest iLAEAT tertile were older, had higher CHA2DS2VASC scores, more likely to be male, have greater LA volume, and were more likely to have persistent (vs. paroxysmal) type AF than participants in the lowest iLAEAT tertile (p for all < 0.05). In regression analyses, iLAEAT was associated with higher odds of AF recurrence (OR = 2.93; 95% CI 1.34-6.43). Conclusions: iLAEAT can quantify LA adipose tissue burden using standard CT images. It is strongly associated with AF risk factors and outcomes, supporting the hypothesis that EAT plays a role in the pathophysiology of AF.

Epicardial adipose tissue: new parameter for cardiovascular risk assessment in high risk populations

Epicardial adipose tissue (EAT) is localized between the myocardial surface and visceral layer of the pericardium. It is a metabolically active organ that secretes several cytokines which modulate cardiovascular morphology and function. EAT may interact locally with coronary arteries through paracrine secretion mechanisms. Cytokines from peri-adventitial EAT may pass through the coronary wall by diffusion from the outside to the inside, interacting with cells. An additional potential mechanism by which EAT interacts locally with coronary arteries may be the vasocrine secretion.EAT may play a significant role as a modulator of cardiac functions. In physiologic conditions, EAT has biochemical cardio-protective properties, secreting anti-atherosclerosis substances; in metabolic disease states, EAT secretes bioactive molecules that may play an important role in the pathogenesis of coronary artery disease and cardiac arrhythmias by promoting atherosclerosis. EAT has been evaluated both in the general population and in metabolic disease states that are characterized by inflammation, such as cardiovascular diseases and chronic kidney disease.This review focuses on the current state of knowledge on EAT as a reliable new parameter for cardiovascular risk stratification in high risk populations.

Measurement of fat fraction in the human thymus by localized NMR and three-point Dixon MRI techniques

PURPOSE

To develop a protocol to non-invasively measure and map fat fraction, fat/(fat+water), as a function of age in the adult thymus for future studies monitoring the effects of interventions aimed at promoting thymic rejuvenation and preservation of immunity in older adults.

MATERIALS AND METHODS

Three-dimensional spoiled gradient echo 3T MRI with 3-point Dixon fat-water separation was performed at full inspiration for thymus conspicuity in 36 volunteers 19 to 56 years old. Reproducible breath-holding was facilitated by real-time pressure recording external to the console. The MRI method was validated against localized spectroscopy in vivo, with ECG triggering to compensate for stretching during the cardiac cycle. Fat fractions were corrected for T₁ and T₂ bias using relaxation times measured using inversion recovery-prepared PRESS with incremented echo time.

RESULTS

In thymus at 3 T, T_1water = 978 ± 75 ms, T_1fat = 323 ± 37 ms, T_2water = 43.4 ± 9.7 ms and T_2fat = 52.1 ± 7.6 ms were measured. Mean T₁-corrected MRI fat fractions varied from 0.2 to 0.8 and were positively correlated with age, weight and body mass index (BMI). In subjects with matching MRI and MRS fat fraction measurements, the difference between these measurements exhibited a mean of -0.008 with a 95% confidence interval of (0.123, -0.138).

CONCLUSIONS

3-point Dixon MRI of the thymus with T₁ bias correction produces quantitative fat fraction maps that correlate with T₂-corrected MRS measurements and show age trends consistent with thymic involution.

Ossabaw Pigs With a PCSK9 Gain-of-Function Mutation Develop Accelerated Coronary Atherosclerotic Lesions: A Novel Model for Preclinical Studies

BACKGROUND

Ossabaw pigs are unique miniature swine with genetic predisposition to develop metabolic syndrome and coronary atherosclerosis after extended periods receiving atherogenic diets. We have hypothesized that transgenic Ossabaw swine expressing chimp PCSK9 (proprotein convertase subtilisin-like/kexin type 9) containing the D374Y gain of function would develop familial hypercholesterolemia and coronary artery plaques more rapidly than Landrace swine with the same transgene.

METHODS AND RESULTS

Ossabaw and Landrace PCSK9 gain-of-function founders were generated by Sleeping Beauty transposition and cloning. Histopathologic findings in the Ossabaw founder animal showed more advanced plaques and higher stenosis than in the Landrace founder, underscoring the Ossabaw genetic predisposition to atherosclerosis. We chose to further characterize the Ossabaw PCSK9 gain-of-function animals receiving standard or atherogenic diets in a 6-month longitudinal study using computed tomography, magnetic resonance (MR) imaging, intravascular ultrasound, and optical coherence tomography, followed by pathological analysis of atherosclerosis focused on the coronary arteries. The Ossabaw model was consistently hypercholesterolemic, with or without dietary challenge, and by 6 months had consistent and diffuse fibrofatty or fibroatheromatous plaques with necrosis, overlying fibrous caps, and calcification in up to 10% of coronary plaques.

CONCLUSIONS

The Ossabaw PCSK9 gain-of-function model provides consistent and robust disease development in a time frame that is practical for use in preclinical therapeutic evaluation to drive innovation. Although no animal model perfectly mimics the human condition, this genetic large-animal model is a novel tool for testing therapeutic interventions in the context of developing and advanced coronary artery disease.

Leveraging the coronary calcium scan beyond the coronary calcium score

Non-contrast cardiac computed tomography in order to obtain the coronary artery calcium score has become an established diagnostic procedure in the clinical setting, and is commonly employed in clinical and population-based research. This state-of-the-art review paper highlights the potential gain in information that can be obtained from the non-contrast coronary calcium scans without any necessary modifications to the scan protocol. This includes markers of cardio-metabolic health, such as the amount of epicardial fat and liver fat, but also markers of general health including bone density and lung density. Finally, this paper addresses the importance of incidental findings and of radiation exposure accompanying imaging with non-contrast cardiac computed tomography. Despite the fact that coronary calcium scan protocols have been optimized for the visualization of coronary calcification in terms image quality and radiation exposure, it is important for radiologists, cardiologists and medical specialists in the field of preventive medicine to acknowledge that numerous additional markers of cardio-metabolic health and general health can be readily identified on a coronary calcium scan.

KEY POINTS

• The coronary artery calcium score substantially increased the use of cardiac CT. • Cardio-metabolic and general health markers may be derived without changes to the scan protocol. • Those include epicardial fat, aortic valve calcifications, liver fat, bone density, and lung density. • Clinicians must be aware of this potential additional yield from non-contrast cardiac CT.

Longitudinal Associations of Pericardial and Intrathoracic Fat With Progression of Coronary Artery Calcium (from the Framingham Heart Study)

Cross-sectional studies have shown that pericardial fat is associated with atherosclerotic burden above and beyond generalized and central adiposity. Whether pericardial fat is longitudinally associated with coronary artery calcium (CAC) has not been firmly established. We examined the associations between cardiac ectopic fat including pericardial and intrathoracic fat with CAC progression and incidence in a community-based study setting. Study participants were from the Framingham Heart Study Offspring and Third Generation Cohorts who underwent multidetector computed tomography at 2 consecutive examinations (2002 to 2005 and 2008 to 2011) for the assessment of CAC. Multivariable-adjusted regression models were used to evaluate the associations between cardiac ectopic fat with CAC. Nonlinear associations were also examined. We included 1,732 participants (49.6% women, mean age 49.9 years). Of the 1,024 participants with a CAC score = 0 at baseline, 197 individuals developed a CAC score > 0 (19.2%) during 6.1 years of follow-up. The remaining 708 participants with a CAC score > 0 at baseline were eligible for CAC progression analysis. We identified nonlinear association between pericardial fat and CAC progression. Higher pericardial fat was associated with higher CAC progression only for those participants with pericardial fat higher than the median value (β = 56.0, p = 0.04). Intrathoracic fat was linearly associated with CAC progression (β = 23.0, p = 0.02). However, all of these associations did not persist after additional adjustment for body mass index, abdominal visceral adipose tissue, or waist circumference (all p ≥ 0.14). Neither pericardial nor intrathoracic fat were associated with CAC incidence (all p ≥ 0.33). Overall, both of the cardiac ectopic fat measures were longitudinally associated with CAC progression.

Anterior mediastinal tissue volume is correlated with antiacetylcholine receptor antibody level in myasthenia gravis

OBJECTIVES

Extended thymectomy is a treatment option for myasthenia gravis (MG), but the surgical indications are controversial. Pathologic features of the thymus can be used to predict surgical outcomes, but there is no reliable method for evaluating these characteristics preoperatively. The purpose of this study was to determine whether anterior mediastinal tissue volume, as measured via 3-dimensional computed tomography (3DCT) volumetry, correlates with serum anti-acetylcholine receptor antibody (AChRAb) levels in patients undergoing thymectomy for myasthenia gravis. Therefore, we investigated the relationships among anterior mediastinal tissue volume determined by 3DCT volumetry and AChRAb levels.

METHODS

The subjects were 28 patients who underwent extended thymectomy and were enrolled retrospectively. We measured volume of the anterior mediastinum and calculated the volumes of more than -30 Hounsfield units (V_-30) by using 3DCT volumetry and compared them with perioperative AChRAb levels. The significance of their volumes in MG was examined by comparison with 53 patients without MG.

RESULTS

V_-30 values were related to age and were significantly greater in patients with MG than in patients without MG (P < .001). V_-30 values were correlated positively with preoperative AChRAb levels (ρ = 0.505, P = .006) and inversely with the post/preoperative AChRAb ratio (ρ = -0.453, P = .018). The histologic nonadipose tissue ratio was correlated with the V_-30/volume of the anterior mediastinum (ρ = 0.700, P < .001).

CONCLUSIONS

This method for evaluation of the anterior mediastinal tissue volume and AChRAb production may be helpful in establishing a treatment plan for MG.

Relationship of Echocardiographic Epicardial Fat Thickness and Epicardial Fat Volume by Computed Tomography with Coronary Artery Calcification: Data from the CAESAR Study

BACKGROUND AND AIMS

No study has assessed the association between echocardiographic epicardial fat thickness (EFT) and computed tomography (CT)-based epicardial fat volume (EFV) and coronary artery calcification. The aim of this study is to evaluate the association between EFT and EFV and coronary artery calcification.

METHODS

Among the 2,299 individuals enrolled in the CArdiometabolic risk, Epicardial fat, and Subclinical Atherosclerosis Registry (CAESAR) study, 2,276 (1,851 men; mean age 45 ± 8.9 years) who underwent echocardiographic EFT and CT-based EFV measurements and obtained a coronary artery calcium score (CACS) were included in this study.

RESULTS

The overall prevalence of CAC >0 was 19.3%. EFT was significantly correlated with EFV (r = 0.374, p <0.001) but the k statistic showed only slight agreement (k = 0.146, p <0.001). Multivariate regression analyses adjusted for age, sex, body mass index, waist circumference, systolic blood pressure, daily alcohol intake, smoking status, and vigorous exercise and glucose, blood urea nitrogen, uric acid, total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, high-sensitivity C reactive protein, and hemoglobinA1c levels revealed that an increase in the absolute values of EFT and EFV was significantly associated with the presence of coronary artery calcium (ORs [95% CIs], 2.023 [1.282-3.193] and 1.785 [1.173-2.716], respectively) and CACS (standardized β values = 0.082 and 0.061, p = 0.001 and 0.042, respectively).

CONCLUSION

These results show that EFT and EFV are associated with coronary artery calcification in Korean adults despite the relatively weak correlation between EFT and EFV.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

INTRODUCTION

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Ectopic Fat Depots and Coronary Artery Calcium in South Asians Compared With Other Racial/Ethnic Groups

Background

South Asians have a low body mass index and high prevalence of cardiovascular disease (CVD) relative to other racial/ethnic groups. Radiographically detected ectopic fat distribution is better associated with CVD than body mass index. We assessed whether differences in ectopic fat depots explained differences in the prevalence/severity of coronary artery calcium (CAC), a predictor of incident CVD events, among South Asians compared with other racial/ethnic groups.

Methods and Results

We examined the associations of radiographically detected visceral, intermuscular, intrahepatic, and pericardial fat with CAC among adults without baseline CVD. We compared 803 South Asians in the Mediators of Atherosclerosis in South Asians Living in America to 4 racial/ethnic groups in the Multi‐Ethnic Study of Atherosclerosis: 2622 whites, 1893 blacks, 1496 Latinos, and 803 Chinese Americans. We adjusted for body mass index and known CVD risk factors. South Asians had the highest intrahepatic fat and lowest pericardial fat volume (PFV). There was a positive graded association between ectopic fat and higher CAC scores in all the groups with the strongest associations observed with PFV. PFV was independently associated with CAC severity in South Asians (P=0.01) and blacks (P=0.05) and borderline in whites (P=0.06). PFV partially explained the higher CAC burden in South Asians compared with blacks, but not the other racial/ethnic groups.

Conclusions

Differences in PFV explain a small fraction of the higher CAC burden in South Asians. Our findings suggest that ectopic fat depots may not explain the elevated CAC risk in South Asians.

Relationship between indexed epicardial fat volume and coronary plaque volume assessed by cardiac multidetector CT

We explored whether baseline indexed epicardial fat volume (EFVi) and serial changes in EFVi were associated with increase in coronary plaque volume as assessed by multidetector computed tomography.We retrospectively reviewed 87 patients with coronary artery plaque, identified during either baseline or follow-up cardiac computed tomography (CT) examinations. Each plaque volume was measured in volumetric units using a semiautomatic software tool. EFVi was quantified by calculating the total volume of epicardial tissue of CT density -190 to -30 HU, indexed to the body surface area. Clinical cardiovascular risk factors were extracted by medical record review at the time of the cardiac CT examinations. The relationship between EFVi and coronary plaque volume was explored by regression analysis.Although the EFVi did not change significantly from baseline to the time of the follow-up CT (65.7 ± 21.8 vs 66.0 ± 21.8 cm/m, P = 0.620), the plaque volumes were increased significantly on the follow-up CT scans. The annual change in EFVi was not accompanied by a parallel change in coronary plaque volume (P = 0.096-0.500). On univariate analysis, smoking, hypercholesterolemia, 10-year coronary heart disease risk, obesity, and baseline EFVi predicted rapid increases in lipid-rich and fibrous plaque volumes. On multivariate analysis, baseline EFVi (odds ratio = 1.029, P = 0.016) was an independent predictor of a rapid increase in lipid-rich plaque volume.EFVi was shown to be an independent predictor of a rapid increase in lipid-rich plaque volume. However, changes in EFVi were not associated with parallel changes in coronary plaque volume.

Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT

PURPOSE

The authors aimed to develop and validate an automated algorithm for epicardial fat volume (EFV) quantification from noncontrast CT.

METHODS

The authors developed a hybrid algorithm based on initial segmentation with a multiple-patient CT atlas, followed by automated pericardium delineation using geodesic active contours. A coregistered segmented CT atlas was created from manually segmented CT data and stored offline. The heart and pericardium in test CT data are first initialized by image registration to the CT atlas. The pericardium is then detected by a knowledge-based algorithm, which extracts only the membrane representing the pericardium. From its initial atlas position, the pericardium is modeled by geodesic active contours, which iteratively deform and lock onto the detected pericardium. EFV is automatically computed using standard fat attenuation range.

RESULTS

The authors applied their algorithm on 50 patients undergoing routine coronary calcium assessment by CT. Measurement time was 60 s per-patient. EFV quantified by the algorithm (83.60 ± 32.89 cm(3)) and expert readers (81.85 ± 34.28 cm(3)) showed excellent correlation (r = 0.97, p < 0.0001), with no significant differences by comparison of individual data points (p = 0.15). Voxel overlap by Dice coefficient between the algorithm and expert readers was 0.92 (range 0.88-0.95). The mean surface distance and Hausdorff distance in millimeter between manually drawn contours and the automatically obtained contours were 0.6 ± 0.9 mm and 3.9 ± 1.7 mm, respectively. Mean difference between the algorithm and experts was 9.7% ± 7.4%, similar to interobserver variability between 2 readers (8.0% ± 5.3%, p = 0.3).

CONCLUSIONS

The authors' novel automated method based on atlas-initialized active contours accurately and rapidly quantifies EFV from noncontrast CT.