CAREER: Advancing In Vivo Knowledge and Assessment of Cartilage Material Properties With Quantitative MRI

About This Grant

This Faculty Early Career Development (CAREER) award supports studying the relationship between microscopic structure (‘microstructure’) and macroscopic function of cartilage when forces are applied that are similar to what is experienced in everyday life. Cartilage health is important to human health, as the main feature of osteoarthritis is cartilage loss. Osteoarthritis is a painful disease that affects about 30 million adults in the United States. Once a person loses cartilage, there is no treatment to regrow cartilage. Currently, little is known about cartilage microstructure, forces, and function, especially from measurements made in young and middle-aged adults before osteoarthritis usually begins. While new medical imaging tools provide an exciting opportunity to see changes in microstructure before cartilage loss, how much altered microstructure relates to cartilage function is still unknown. Proposed research will improve our current scientific understanding of cartilage and help patients at risk for osteoarthritis in the future. Community outreach events will engage and inspire middle and high school students from Vermont and include hands-on activities that show how changing what a material is made of impacts its function. The research project investigates the links between quantitative magnetic resonance imaging metrics of cartilage microstructure, experimentally derived function of articular cartilage, and statistical modeling of bone shape. The studies supported in this project employ in vivo experiments, in situ testing, and in silico computational simulations. In vivo experimentation will leverage recently developed quantitative magnetic resonance imaging metrics of microstructure in cartilage, as assessed in a traditional ‘unloaded’ state, and bone shape to accurately predict changes with loading and subsequent relaxation. In situ testing will assess the capacity of quantitative magnetic resonance imaging metrics to predict the results of viscoelastic tests. In silico computational simulations will be developed and benchmarked for improving subject-specific computational models of joint mechanics by updating biphasic material properties with image-based measurements of articular cartilage microstructure. Overall, this research work will generate comprehensive and novel understanding of cartilage microstructure, response to load (with subsequent relaxation), and statistical shape modeling of bone to-date, advancing knowledge of multiscale structure-function relationships in articular cartilage by combining imaging experiments, biomechanical tests, and computational simulations. The benchmarked computational framework has the potential to transform subject-specific modeling in articular cartilage, which has broad implications for the field of biomechanics and computational modeling. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.