A Combined Experimental and Computational Approach to Subject-Specific Analysis of Knee Joint Laxity

Abstract

Modeling complex knee biomechanics is a continual challenge, which has resulted in many models of varying levels of quality, complexity, and validation. Beyond modeling healthy knees, accurately mimicking pathologic knee mechanics, such as after cruciate rupture or meniscectomy, is difficult. Experimental tests of knee laxity can provide important information about ligament engagement and overall contributions to knee stability for development of subject-specific models to accurately simulate knee motion and loading. Our objective was to provide combined experimental tests and finite-element (FE) models of natural knee laxity that are subject-specific, have one-to-one experiment to model calibration, simulate ligament engagement in agreement with literature, and are adaptable for a variety of biomechanical investigations (e.g., cartilage contact, ligament strain, in vivo kinematics). Calibration involved perturbing ligament stiffness, initial ligament strain, and attachment location until model-predicted kinematics and ligament engagement matched experimental reports. Errors between model-predicted and experimental kinematics averaged <2 deg during varus-valgus (VV) rotations, <6 deg during internal-external (IE) rotations, and <3 mm of translation during anterior-posterior (AP) displacements. Engagement of the individual ligaments agreed with literature descriptions. These results demonstrate the ability of our constraint models to be customized for multiple individuals and simultaneously call attention to the need to verify that ligament engagement is in good general agreement with literature. To facilitate further investigations of subject-specific or population based knee joint biomechanics, data collected during the experimental and modeling phases of this study are available for download by the research community.

Combined measurement and modeling of specimen-specific knee mechanics for healthy and ACL-deficient conditions

“…In the first step, in-vitro testing replicated a deep knee bend using motor-actuated quadriceps force to calibrate PF mechanics in specimen-specific FE models of the experiment (Ali et al, 2016). In the second step, laxity experiments were performed in the same knees to capture passive constraint of the TF joint (Harris et al, 2016). FE modeling of the laxity experiments allowed calibration of TF soft tissue material properties and attachment locations for intact and ACL-deficient conditions.…”

“…Each knee was subjected to three experiments in intact and ACL-deficient conditions. First, passive TF laxity was measured by manually applying ± 8 Nm internal-external (I-E) torques, ± 10 Nm varus-valgus (V-V) torques, and ± 80 N anterior-posterior (A-P) loads ~300 mm below the joint line at 0–60° knee flexion (Harris et al, 2016). A load cell attached to the proximal end of the tibia recorded 6 DOF loads from each laxity test and provided real-time user feedback via LabView (National Instruments, Austin, TX).…”

“…Calibration of specimen-specific PF mechanics was performed in a muscle-loaded rig (MLR) designed to isolate the quadriceps mechanism during knee flexion (Ali et al 2016). Joint laxity tests were performed on the same specimens to quantify joint constraint and derive optimized TF ligament material properties (Harris et al, 2016). However, these models of the PF and TF articulations of the knee were not combined into a dynamic representation of the natural knee.…”

A novel method for defining ligament characteristics in subject specific dynamic surgical planning

“…Subject specific information is needed in these models to better represent patient variability in the population (Roth et al, 2015;Harris et al, 2016). Few computational model studies (Ewing et al, 2016;Baldwin et al, 2012;Bloemeker et al, 2012) have been able to estimate ligament soft tissue properties of cadavers from the knee laxity data recorded experimentally.…”

“…Often, the details in published models varied considerably, including the number and complexity of included structures, material behaviour, or the use of generic versus subject-specific representations of bone and cartilage geometry (Harris et al, 2016). Regardless of the complexity, subject specific information has been identified as important inputs to knee models (Roth et al, 2015;Harris et al, 2016). Most TKR computational models use either CT or MRI as input.…”