Statistical Shape Modeling of the Knee
Osteoarthritis affects 17.5 million people in the United States and results in more than 300,000 total knee replacements (TKR) each year. In the natural knee, joint mechanics provide insight into musculoskeletal function and are used to assess pathologies. In the implanted knee, resultant post-operative joint mechanics contribute to the success of TKR, which is influenced by implant design and component alignment. The most significant source of uncertainty in prior knee mechanics studies is patient-to-patient variability, yet it remains the least understood. Subject-specific models developed from imaging data provide the fidelity required to accurately represent anatomical structures. To then account for variability within a population, statistical shape models have been created to characterize the modes of variation across subjects. Current state-of-the-art analyses have developed shape models of individual bones, but have not considered shape models with multiple structures like a joint, nor integrated shape models into joint mechanics prediction. Accordingly, the objective of this project was to develop a computational methodology for the population-based evaluation of natural and implanted joint mechanics considering inter-subject geometric, property and surgical variability.