<p>  </p> <p>Cardiovascular disease has been the leading cause of death in the United States for over 70 years. To evaluate the extent and progression of cardiovascular disease, non-invasive imaging techniques are frequently used clinically and pre-clinically. Current echocardiographic and cine magnetic resonance approaches rely on measurements that are typically obtained from two-dimensional images, which assumes uniformity of the structure being evaluated. To explore methods to potentially address these shortcomings, our group has developed and validated high frequency four-dimensional ultrasound techniques as well as created a software toolbox that allows for measurement of myocardial kinematics. In this thesis, I assisted in the application of these methods to two murine models of disease states: myocardial infarction and aortic aneurysm. Another study I aided in focused on cardiac magnetic resonance imaging data from patients with Duchenne muscular dystrophy. From our software, we are able to obtain various strain and strain rate estimates that reveal significant functional changes in infarction and Duchenne muscular dystrophy earlier than standard measurement techniques. Furthermore, we are able to identify vascular expansion, transmural thickening, and changes in hemodynamics prior to aneurysm development. Earlier detection and localization allows for more targeted surveillance and interventions, which ultimately may result in improved clinical outcomes. Ideally, these findings can be used to expand the capabilities of cardiac research and the development of clinically applicable imaging techniques and treatments to better address underlying cardiovascular pathophysiology. </p>