Model-based assessment of cardiac adipose tissue volume and distribution

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Project Summary: Obesity continues to be one of the most important health issues of our time and is a significant risk factor for cardiovascular disease. A layer of fat called epicardial adipose tissue (EAT) forms between the myocardium and visceral pericardium. In addition, pericardial adipose tissue (PAT) forms between the visceral and parietal pericardium. This combined fat deposit, the cardiac adipose tissue (CAT), directly influences the development of coronary artery disease. Therefore, measuring the volume and distribution of CAT can be a marker for coronary artery disease and has become an important task for cardiovascular risk assessment. Cardiac magnetic resonance imaging (MRI) can provide three-dimensional (3D) assessment of CAT, but it is expensive, time-consuming, and is only available at large institutions. Echocardiography is safe, real-time, inexpensive, and can also be used to quantify cardiac structure and function. The goal of this project is to use real-time echocardiography and advanced processing of the radiofrequency (RF) ultrasound signals for volumetric assessment of CAT. Cardiac MRI will be used to build a surface map and 3D model of the fat that will serve as the foundation for fusion of the echocardiography. The fat identified in the echocardiography will then be used to warp the model to match the fat present in the ultrasound images, as identified through processing of the RF signals. Leveraging the specific individual strengths of MRI and echocardiography has the potential to yield a widely available and less expensive measurement system for CAT volume, a potential marker for coronary artery disease and cardiovascular risk. We will manually segment the left ventricle, right ventricle, and the layer of CAT in the cardiac MRI scans. The segmented fat will be used to create an average surface map and 3D model of the CAT that will be incorporated into an open-source, “average” heart model. The contours from the ventricles identified in the cardiac MRI will be used to align the ultrasound image planes and fuse them with the model. This step will be performed by registering the left-ventricular contours identified in the echocardiography images with those from the MRI. Next, the CAT in a sequence of short-axis 2D echocardiographic images will be localized using spectral analysis of the raw radiofrequency signals. It will be used to guide the deformation of the 3D model to match the fat identified in the echocardiography, resulting in a volume and distribution of CAT in each individual subject. The outcome of the project will be a set of tools, based on ultrasound alone, which can be used to measure CAT volume and potentially provide a marker for coronary artery disease and cardiovascular risk.