Abstract
INTRODUCTION
Understanding the relationship between knee specific tissue behavior and joint contact mechanics remains an area of focus. Seminal work from 1990's established the possibility to optimize tissue properties for recreation of laxity driven kinematics (Mommersteeg et al., 1996). Yet, the uniqueness and validity of such predictions could be strengthened, especially as they relate to joint contact conditions. Understanding this interplay has implications for the long term performance of joint replacements.
Development of instrumented knee implants, highlighted by a single use tibial insert trial with embedded sensor technology (VERASENSE, Orthosensor Inc.), may offer an avenue to establish the relationship between tissue state and joint mechanics. Utilization of related data also has the potential to confirm computational predictions, where both rigid body motions and associated reactions are explicitly accounted for. Hence, the goal of this work was to evaluate an approach for optimization of ligament properties using joint mechanics data from an instrumented implant during laxity style testing. Such a framework could be used to inform joint balancing techniques, improve long term implant performance, and alternatively, qualify factors that may lead to poor outcomes
METHODS
Experimentation was performed on a 52 year old male, left, cadaveric specimen. Joint arthroplasty was performed using standard practice by an experienced orthopedic surgeon. To mimic passive intraoperative loading, laxity loading at 10°, 45° and 90° flexion, which consisted of discrete application of anterior-posterior (± 100N), varus-valgus (± 5 Nm) and internal-external (± 3 Nm) loads at each angle, was performed using a simVITROTM robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH). Experimental results included relative tibiofemoral kinematics and sensor measured metrics (Fig 1).
The finite element model was developed from specimen-specific MRIs and solved using Abaqus/Explicit. The model included the rigid bones, appropriately placed implants and relevant soft-tissue structures (Fig. 1). Ligament stiffness values were adopted from the literature and included a 6% strain toe region. Sets of nonlinear springs, defined using MR imaging, comprised each ligament/bundle. Optimization was performed, which minimized the root mean squared difference between VERASENSE measured tibiofemoral mechanics and the model predicted values. Ligament slack lengths were the control variables and the objective included each loading state and all contact metrics (θ, AFD, ML, and LL).
RESULTS AND DISCUSSION
The model successfully recreated joint kinematics with average errors of 4° for rotations and 3 mm for translations, across all flexion angles (Fig 2). Though a systematic offset in θ was observed, model versus experiment contact locations were also in good agreement. Reaction forces were generally over-predicted by the model, but retained the overall trend (Fig 2). Sensitivity analysis also supported this finding. In light of the larger focus of this project, testing also included systematic removal of key tissues followed by repeat testing, as evaluated across numerous specimens. Overall, the presented framework represents a promising step towards establishing simulation based tools able to support exploratory studies as well as the clinical decision making process. Future work will evaluate efficacy across numerous specimens and assess sensitivity to key modeling and experimental parameters.
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