Abstract
Despite of the high success of TKA, 20% of recipients remain dissatisfied with their surgery. There is an increasing discordance in the literature on what is an optimal goal for component alignment. Furthermore, the unique patient specific anatomical characteristics will also play a role. The dynamic characteristic of a TKR is a product of the complex interaction between a patient's individual anatomical characteristics and the specific alignment of the components in that patient knee joint. These interactions can be better understood with computational models. Our objective was to characterise ligament characteristics by measuring knee joint laxity with functional radiograph and with the aid of a computational model and an optimisation study to estimate the subject specific free length of the ligaments.
Pre-operative CT and functional radiographs, varus and valgus stressed X-rays assessing the collateral ligaments, were captured for 10 patients. CT scan was segmented and 3D–2D pose estimation was performed against the radiographs. Patient specific tibio-femoral joint computational model was created. The model was virtually positioned to the functional radiograph positions to simulate the boundary conditions when the knee is stressed. The model was simulated to achieve static equilibrium. Optimisation was done on ligament free length and a scaling coefficient, flexion factor, to consider the ligaments wrapping behaviour.
Our findings show the generic values for reference strain differ significantly from reference strains calculated from the optimised ligament parameters, up to 35% as percentage strain. There was also a wide variation in the reference strain values between subjects and ligaments, with a range of 37% strain between subjects. Additionally, the knee laxity recorded clinically shows a large variation between patients and it appears to be divorced from coronal alignment measured in CT. This suggests the ligaments characteristics vary widely between subjects and non-functional imaging is insufficient to determine its characteristics. These large variations necessitate a subject-specific approach when creating knee computational models and functional radiographs may be a viable method to characterise patient specific ligaments.
