Epicardial adipose tissue (EAT) is the layer of fat that accumulates around the myocardium of the heart and is a contributing factor to cardiovascular disease. Identification and quantification of this fat depot is important in ongoing studies of intervention. We have manually traced the EAT in 20 cardiac MRI scans, but this process is tedious and time-consuming. The goal of this project was to develop a segmentation algorithm that would shorten the time it takes to quantify the EAT. The validation data consisted of pre-intervention and post-intervention MRI scans from 12 (4 subjects did not have post-intervention scans) volunteer female subjects. The EAT, myocardium, and ventricles were manually traced in each slice of each scan. For the automated algorithm, preprocessing consisted of transforming the image data to the polar domain using the centroid of the traced inner EAT contour. In the polar image, each radial angle contained an inner-contour point and an outer-contour point, identifying the thickness of the fat at that radial location in that slice. These two locations on each single angle view served as the input for the neural network along with the angle, the slice location, and time in the cardiac cycle including either end-diastole or end-systole. Two neural networks were trained, one for the inner edge of EAT and a second for the outer edge of EAT. The networks returned the location of the contours in each radial angle and this was compared with the traced solutions. The mean dice similarity coefficient for the automatically identified EAT vs. the manually traced EAT was 0.56 ± 0.12. The current algorithm produces promising results that warrant further investigation and development.
Cardiac Adipose Tissue (CAT) is a type of visceral fat that is deposited between the myocardium and pericardium. An increased volume of CAT has been recognized as a crucial contributor to cardiovascular and coronary artery diseases. This tissue is a metabolically active organ that affects the cardiac functioning by secreting inflammatory adipokines making it a hazard when present in excess amounts. Quantifying CAT, therefore, can be an important factor in understanding the level of cardiovascular risk. The study presented in this paper investigates the use of frequency content from echocardiography and spectral analysis techniques in differentiating three different cardiac tissue types, including the adipose tissue. Thirteen spectral parameters were computed from the power spectrum of the radio frequency data in three different bandwidth ranges, including 3, 6 and 20 dB. Autoregressive models of order 4 were used as they provide effective estimates of the power spectrum for short-time data. The derived spectral parameters were used in generating random forests for tissue classification. Out of the total 175 ROIs available, 70% of the data was divided into training data and the remaining used as test data. The random forest classifier with 50 classification trees resulted in an overall accuracy of 92.4%, sensitivity of 91.1%, specificity of 93.9%, and Youden’s index of 0.85 for a 20dB bandwidth. This result demonstrates the potential of echocardiography and spectral analysis techniques in differentiating CAT, myocardium, and blood.
Magnetic resonance imaging (MRI) has evolved into the gold standard for quantifying excess adiposity, but reliable, efficient use in longitudinal studies requires analysis of large numbers of images. The objective of this study is to develop and evaluate a segmentation method designed to identify cardiac, subcutaneous, and visceral adipose tissue (VAT) in Dixon MRI scans. The proposed method is evaluated using 10 scans from volunteer females 18- to 35-years old, with body mass indexes between 30 and 39.99 kg / m2. Cross-sectional area (CSA) for cardiac adipose tissue (CAT), subcutaneous adipose tissue (SAT), and VAT, is compared to manually-traced results from three observers. Comparisons of CSA are made in 191 images for CAT, 394 images for SAT, and 50 images for VAT. The segmentation correlated well with respect to average observer CSA with Pearson correlation coefficient (R2) values of 0.80 for CAT, 0.99 for SAT, and 0.99 for VAT. The proposed method provides accurate segmentation of CAT, SAT, and VAT and provides an option to support longitudinal studies of obesity intervention.
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