Population-based evaluation of knee mechanics considering inter-subject and surgical alignment variability

University Of Denver, Denver CO

Investigators

Abstract

PI: Laz, Peter J. and Rullkoetter, Paul Proposal Number: 1034251 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 proposal is to develop a computational methodology for the population-based evaluation of natural and implanted joint mechanics considering inter-subject geometric, property and surgical variability. The specific aims are: -{nTo characterize geometric variability in a population of subjects with a statistical shape model of the structures of the knee. -{nTo evaluate variability in natural and TKR-implanted knee mechanics in a population using a probabilistic platform combining the statistical shape model with uncertainty in mechanical properties, e.g. ligament stiffness, and surgical parameters, e.g. alignment of a TKR component. -{nTo identify relationships between geometric variability and natural joint mechanics and between component alignment, geometric variability, and implanted joint mechanics using a novel combined probabilistic and principal component analysis technique. The project will develop the statistical shape model of the knee from magnetic resonance image datasets from the Osteoarthritis Initiative and perform probabilistic finite element assessments of joint mechanics in natural and implanted knees under a simulated deep flexion loading condition. Intellectual merit of the proposed activity The intellectual merit of the proposed activity is in the novel development of a statistical shape modeling of the knee joint and the creation of a novel integrated framework where the effects of geometric and other uncertainties on joint mechanics can be holistically assessed. The population-based predictions can address concepts of form and function in the natural knee and provide insight into interdependencies between patient geometry and TKR component alignment and their impact on knee mechanics. Broader impacts resulting from the proposed activity The broader impacts are related to the dissemination of the findings by creating a website to allow the statistical shape model of the knee to be downloaded by other researchers and the publication of the newly developed methodologies. Additionally, based on the highly visual and relevant nature of the project, interactive curricular components will be developed, implemented in the Making of an Engineer outreach program which promotes engineering and the sciences to underrepresented minorities, and made publicly available to assist teachers in creating and carrying out science, technology, engineering and mathematics (STEM) curricula.