BME PhD Defense Announcement for Katherine Leyba (Dr. Craig Goergen, Advisor) Everyone is invited to attend the public presentation beginning at 10:00 am. Research title: Optical and Acoustic-based Imaging Methods for Quantification of Oxygenation and Strain in Murine Cardiovascular Disease Models Thesis Committee members: Dr. Craig Goergen (Chair) Dr. Fang Huang Dr. Charles Bouman Dr. Pierre Sicard Date: March 30, 2023 Time: 10:00 am Location: MJIS 2001 or https://purdue-edu.zoom.us/j/95670493593?pwd=cHV1aUJHOTRJSjlvUGx2NUhEZkw4UT0... Abstract: Cardiovascular disease (CVD) is the leading cause of death worldwide and is expected to increase direct medical costs in the U.S. to $749 billion by the year 2035. Diagnosis of CVD through imaging techniques can improve our understanding of CVD progression and its associated risks through visualization of anatomical features and biological constituents. Non-invasive imaging relies on optimal image quality for visualization of such tissue structures that can be difficult to identify and segment. While various imaging modalities are used to determine tissue characteristics, many lack the spatial resolution that optics-based imaging can provide, which can assess hemodynamic parameters in preclinical models of ischemic disease. Acoustic-based imaging can complement optics-based imaging by providing anatomical and location-specific information of tissues with greater penetration depth. Even with all the advancements in imaging technology, however, limitations still exist in non-invasively, efficiently, and accurately capturing biologically relevant information with adequate spatial and temporal resolution. Furthermore, reproducible feature extraction is difficult due to a lack of standardization in the field, making it difficult to implement when image quality varies. In this work, we implement spatial frequency domain imaging (SFDI), ultrasound, and photoacoustic imaging in preclinical models of 1) peripheral artery disease, 2) traumatic brain injury, and 3) myocardial ischemia to capture imaging biomarkers of vascular and cardiac health in longitudinal studies. We also implement deep learning on preclinical ultrasound and photoacoustic images of the cardiac left ventricle to automatically extract regions of interest to calculate radial strain and oxygen saturation. Eventually findings from this work may help improve clinical cardiovascular disease diagnosis, prognosis, and treatment.