RESEARCH PROJECT - Chronic myocardial infarction and heart failure with preserved ejection fraction affect millions of patients in the US. These cardiac pathologies impair the mechanical performance of the heart and may lead to breathlessness, exercise intolerance, and severe fatigue. Both are associated with an increase in passive myocardial stiffness, but despite the widespread clinical presentation and dire prognosis associated with these cardiac diseases, our ability to accurately characterizes changes in stiffness remains profoundly limited. Our understanding is limited, in part, because the available measures of ?cardiac stiffness? are both poorly defined and based on assumptions that depend, in part, on ventricular geometry instead of changes to tissue microstructure. In addition,the current material models describing myocardial stiffness are based on ex vivo data, and therefore do reflect the in vivo environment and conditions. Thegoal of this researchis to develop robust definitions of myocardial stiffness and measures of cardiac kinematics (strain) that are needed to transform patient-specific MRI data into quantitative diagnostic measures of diastolic stiffness in patients with cardiovascular diseases. In order to achieve this objective, we will first identify strain measures that characterize the kinematics of passive filling(Specific Aim-1). Then, we will use these in vivo kinematic measures, combined with intraventricular pressure data, to robustly define a measure of diastolic myocardial stiffness in vivo(Specific Aim-2). Lastly, to validate our new myocardial stiffness formulation and test its diagnostic capabilities, we will identify distinct differences in local myocardial stiffness between infarcted, border zone, and remote myocardium (Specific Aim-3). This work will lead to a new approach that can leverage patient specific MRI and pressure data to extract quantitative kinematic and passive stiffness diagnostic biomarkers. These biomarkers will enable a rigorous diagnosis of pathologies such as heart failure with preserved ejection fraction (HFpEF) and quantitatively characterize the impact of myocardial remodeling (e.g., due to an infarct) on diastolic myocardial stiffness. CANDIDATE CAREER GOALS AND DEVELOPMENT PLAN -An advanced understanding of the medical and clinical aspects of cardiac mechanics and kinematics is essential to validate and evaluatenumerical models against available data, interpret the model results, and propose new research directions. In this award I plan to acquire the necessary medical and clinical knowledge to complement my numerical analysis capabilities and allow me to pursue my research in translational cardiac biomechanics. This multidisciplinary skill set will enable me to start an independent professorial appointment in academia, which is my main career goal. During the duration of this award, I plan to: 1) significantly increase my knowledge of cardiac anatomy, physiology, and pathophysiology by attending a cardiovascular physiology course, participating in Dr. Ennis's (mentor) and Dr. Garfinkel's (co-mentor) weekly group meetings, and attending Cardiology and Radiology Grand Rounds; 2)Improve my understanding of the physics of cardiac MRI and data processing by attending Dr. Ennis's graduate level MRI physics course, observe clinical cardiac MRI exams, processing cine DENSE and cardiac DT-MRI data, and acquiring MRI data for animal experiments in Year 2 and 3; 3)Learn how to present my research results to a medical audience - largely different from the engineering community in which I currently work - by presenting my research results at weeklyclinical conferences, group meetings with MRI experts, and in medically oriented professional conferences (e.g., AHA); and 4) Establish independence and leadership skills by directing research projects with undergraduate students and discussing with Dr. Ennis how to prioritize the many research projects, applications, presentations, and other duties. ENVIRONMENT -Dr. Ennis's (mentor), Dr. Garfinkel's (co-mentor), and Dr. Demer's (co-mentor) research experience and the overall research environment at UCLA are exceptionally well suited to support the presented research plan. Dr. Ennis has extensive experience in all the core areas involved in the presented research. He has worked for many years in cardiac and cardiovascular magnetic resonance imaging, finite element modeling and material property estimation of the cardiovascular system, and cardiac function and microstructure. Dr. Garfinkel has extensive experience in mathematical modeling of biological systems, with particular emphasis on modeling cardiac electrophysiology. Moreover, Dr. Garfinkel is an expert in statistical analysis applied to biological problems. Dr. Perotti (PI) will greatly benefit from working with Dr. Garfinkel to improve his approach to cardiac modeling and incorporate rigorous statistical analyses in his research.Dr. Demer's is an expert in theoretical and experimental aspects of the mechanical response of cardiovascular tissues to a range of loading conditions.Dr. Perotti will be able to discuss with Dr. Demerthe physiological features that must be captured in his computational model and the medical implications of changes in myocardial kinematics and stiffness. Collaborating with her will ensure the project stays clinically focused. UCLA itself is ideal for carrying out the research plan described in this proposal. Few centers can offer the proximity of physical, educational,and intellectual resources that can be found at UCLA, including proven collaborations between the medical school, bioengineering, and mechanical engineering and the world-class MRI experimental facilities and computational resources.
Chronic myocardial infarction and heart failure with preserved ejection fraction affect millions of patients in the US and lead to breathlessness, exercise intolerance, and severe fatigue. However, due to a poor characterization of the underlying motion patterns and inadequate definition of the heart's stiffness, the quantitative impact of these pathologies on the heart's mechanical performance is still poorly measured and understood. Our work seeks to develop and validate a new approach to characterizing cardiac motion and stiffness and, based on a deeper understanding, provide quantitative diagnostic biomarkers that may better inform therapeutic treatment options for millions of patients.