Magnetic resonance imaging (MRI) can provide three-dimensional (3D) assessment of EAT, 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 utilize 3D volumetric information from MRI data to develop a shape-based model to be used in conjunction with real-time echocardiography and advanced processing of the radio-frequency (RF) ultrasound signals for volumetric assessment of EAT. Machine learning algorithms are used to differentiate tissue types based on features from the ultrasound spectra. Leveraging the specific individual strengths of MRI and echocardiography has the potential to yield a more powerful, yet less expensive analysis tool suited for large studies of intervention and their effect on EAT and cardiovascular health.