BME PhD Preliminary Exam Announcement for Alexander Donabedian (D. Chan, advisor) Everyone is invited to attend the public presentation beginning at 2:00pm. Research Title: Subject-Specific Elastic, Fiber, and Swelling Spatial Variation in Finite Element Models of Articular Cartilage Informed by MRI Date, Time, Place: October 29, 2024 @ 2:00pm in MJIS 2001 Committee: Dr. Deva Chan (advisor), Dr. Chad Carroll, Dr. Vitaliy Rayz, Dr. Adrian Buganza Tepole Abstract: Osteoarthritis (OA) is a disabling joint disease that continues to prevail among the US population. Without a true noninvasive cure, total joint replacement surgery is a likely outcome which is costly for the healthcare system and individual. To shift treatment towards prevention and away from palliation, computational models can be a tool to study articular cartilage at risk of degradation, a hallmark sign of OA progression, before reaching severe states of the disease. Due to the complex microstructure of articular cartilage, generalized models cannot reliably provide subject-specific information regarding the health of the patient's tissue. Finite element models can be tailored to the patient with magnetic resonance imaging (MRI) improving their reliability. MRI-informed models exist today; however, very few of them address the spatially varying characteristics of articular cartilage hindering their accuracy. Here, we focus on using MRI to inform cartilage constituents primarily responsible for its tensile and compressive resistance in finite element models. Specifically, regional variations in stiffness, collagen fiber orientations, and osmotic swelling are informed by quantitative MRI techniques. Simulating the combined contribution of spatially varying properties is studied against models with generalized properties found in literature. Additionally, computed areas of high stress and strain are compared with longitudinal cartilage health data to assess the model's potential to predict regional degradation. By tailoring the simulated cartilage material properties and microstructure to the patient, the models can more reliably pinpoint areas at risk for degradation to potentially signal the onset of OA. -- Bmeroundtable-list mailing list Bmeroundtable-list@ecn.purdue.edu https://engineering.purdue.edu/ECN/mailman/listinfo/bmeroundtable-list