Purpose: The coronary arteries are embedded in a layer of fat known as epicardial adipose tissue (EAT). The EAT influences the development of coronary artery disease (CAD), and increased EAT volume can be indicative of the presence and type of CAD. Identification of EAT using echocardiography is challenging and only sometimes feasible on the free wall of the right ventricle. We investigated the use of spectral analysis of the ultrasound radiofrequency (RF) backscatter for its potential to provide a more complete characterization of the EAT.
Approach: Autoregressive (AR) models facilitated analysis of the short-time signals and allowed tuning of the optimal order of the spectral estimation process. The spectra were normalized using a reference phantom and spectral features were computed from both normalized and non-normalized data. The features were used to train random forests for classification of EAT, myocardium, and blood.
Results: Using an AR order of 15 with the normalized data, a Monte Carlo cross validation yielded accuracies of 87.9% for EAT, 84.8% for myocardium, and 93.3% for blood in a database of 805 regions-of-interest. Youden’s index, the sum of sensitivity, and specificity minus 1 were 0.799, 0.755, and 0.933, respectively.
Conclusions: We demonstrated that spectral analysis of the raw RF signals may facilitate identification of the EAT when it may not otherwise be visible in traditional B-mode images.
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.
The fat that accumulates between the myocardium and the visceral pericardium is called epicardial adipose tissue (EAT). When volume is increased, the EAT can secrete chemicals that influence the development of coronary disease. Volumetric assessment of magnetic resonance imaging (MRI) can quantify EAT, but volume alone gives no information about its distribution across the myocardial surface. In this study, a three-dimensional (3D) modeling technique is developed and used to quantify the distribution of the EAT across the surface of the heart. Dixon MRI scans, which produce a registered 3D set of fat-only and water-only images, were acquired in 11 subjects for a study on exercise intervention. A previously developed segmentation algorithm was used to identify the heart and EAT in six of the scans. Contours were extracted from the labeled images and imported into NX 10, where 3D models of both surfaces were created. Procrustes analysis was used to register the heart models and create an average heart surface. An iterative closest point algorithm was used to register and align the EAT models for calculation of EAT thickness. Rays were cast in directions specified by a spherical parameterization of elevation and azimuthal angles, and intersections of the ray with the EAT surface were used to calculate the thickness at each location. The thickness maps were averaged and then “painted” onto the average heart model, creating a single, integrated model representing the average EAT thickness across the surface of the myocardium.
Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) together have become the gold
standard in the precise quantification of body fat. The study of the quantification of fat in the human body has matured in
recent years from a simplistic interest in the whole-body fat content to detailing regional fat distributions. The realization
that body-fat, or adipose tissue (AT) is far from being a mere aggregate mass or deposit but a biologically active organ
in and of itself, may play a role in the association between obesity and the various pathologies that are the biggest health
issues of our time. Furthermore, a major bottleneck in most medical image assessments of adipose tissue content and
distribution is the lack of automated image analysis. This motivated us to develop a proper and at least partially
automated methodology to accurately and reproducibly determine both body fat content and distribution in the human
body, which is to be applied to cross-sectional and longitudinal studies. The AT considered here is located beneath the
skin (subcutaneous) as well as around the internal organs and between muscles (visceral and inter-muscular). There are
also special fat depots on and around the heart (pericardial) as well as around the aorta (peri-aortic). Our methods focus
on measuring and classifying these various AT deposits in the human body in an intervention study that involves the
acquisition of thoracic and abdominal MR images via a Dixon technique.
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