Loads acting on the knee are tied to the long term performance of implants, and are directly related to ligament function [1]. Previous work has used computational models coupled with optimization to estimate ligament properties based on experimental joint kinematics [2]. Our group recently utilized a similar optimization scheme that estimated ligament slack lengths based on experimental implant contact metrics [3]. A comparison with surgically relevant loading conditions that were excluded from the optimization would help establish the utility of the simulation framework. Hence, the purpose of this study was to assess the predictive capability of two simulated knees using comparisons with experimentally determined trends found after systematic removal of key tissues. Similar techniques may support clinical joint balancing techniques, as well as identify factors that dictate long term implant performance. Knee arthroplasty was performed by orthopedic surgeons for four cadaveric specimens. Instrumented trial inserts (VERASENSE, OrthoSensor, Inc., Dania Beach, FL) were used and experimentation utilized the simVITROTM robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH) to measure tibiofemoral kinematics under interoperative style loading. Three successive laxity style tests were performed at 10° flexion: anterior-posterior force (±100 N), varus-valgus moment (±5 Nm), and internal-external moment (±3 Nm). Kinematics and implant forces were measured throughout testing. Specimens were first tested in the intact state, then the laxity tests were repeated after systematic release of the posterior cruciate ligament (PCL), superficial medial collateral ligament (sMCL), or popliteus (POP). Significant changes in kinematics and contact metrics were determined using regression analysis between the intact versus the tissue released states. Finite element models were developed for two specimens, and optimized ligament slack lengths were found using methods described previously [3] (Fig. 1). The experimental laxity style loads were applied to both optimized models with intact ligaments, and with individually released PCL, sMCL, or POP ligaments. Knee kinematics and tibial contact loads were predicted, and trended responses from the intact simulations to those with released ligaments were determined (i.e. higher, lower or no change). Simulation results were then compared with the statistically significant findings from the experimental tests.Introduction
Methods
Joint mechanics and implant performance have been shown to be sensitive to ligament properties [1]. Computational models have helped establish this understanding, where optimization is typically used to estimate ligament properties for recreation of physically measured specimen-specific kinematics [2]. If available, contact metrics from physical tests could be used to improve the robustness and validity of these predictions. Understanding specimen-specific relationships between joint kinematics, contact metrics, and ligament properties could further highlight factors affecting implant survivorship and patient satisfaction. Instrumented knee implants offer a means to measure joint contact data both in-vivo and intra-operatively, and can also be used in a controlled experimental environment. This study extends on previous work presented at ISTA [3], and the purpose here was to evaluate the use of instrumented implant contact metrics during optimization of ligament properties for two specimens. The overarching goal of this work is to inform clinical joint balancing techniques and identify factors that are critical to implant performance. Total knee arthroplasties were performed on 4 (two specimens modeled) cadeveric specimens by an experienced orthopaedic surgeon. An instrumented trial implant (VERASENSE, OrthoSensor, Inc., Dania Beach, FL) was used in place of a standard insert. Experimentation was performed using a simVITROTM controlled robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH) to apply intra-operative style loading and measure tibiofemoral kinematics. Three successive laxity style tests were performed at 10° knee flexion: anterior-posterior force (±100 N), varus-valgus moment (±5 Nm), and internal-external moment (±3 Nm). Tibiofemoral kinematics and instrumented implant contact metrics were measured throughout testing (Fig. 1). Specimen-specific finite element models were developed for two of the tested specimens and solved using Abaqus/Explicit (Dassault Systèmes). Relevant ligaments and rigid bone geometries were defined using specimen-specific MRIs. Virtual implantation was achieved using registration and each ligament was modeled as a set of nonlinear elastic springs (Fig. 1). Stiffness values were adopted from the literature [2] while the ligament slack lengths served as control variables during optimization. The objective was to minimize the root mean square difference between VERASENSE measured tibiofemoral contact metrics and the corresponding model results (Fig. 1).Introduction
Methods
