Forward solution joint models (FSMs) can be powerful tools, leading to fast and cost-efficient simulation revealing i The objective of this study was to use the knee FSM to predict the condylar translations and axial rotation of both fixed- and mobile-bearing TKA designs during a deep knee bend activity and to compare these kinematics to known fluoroscopy evaluations.Introduction
Objective
The overall goal of total knee arthroplasty (TKA) is to facilitate the restoration of native function following late stage osteoarthritis and for this reason it is important to develop a thorough understanding of the mechanics of a normal healthy knee. While there are several methods for assessing TKA mechanics, these methods have limitations that make them prohibitive to both replicating physiological systems and evaluating non-implanted knees. These limitations can be circumvented through the development of mathematical models that use anatomical and physiological inputs to computationally simulate joint mechanics. This can be done in an inverse or forward manner to solve for either joint forces or motions respectively. The purpose of this study is to evaluate one such forward model and determine the accuracy of the predicted motions using fluoroscopy. In vivo kinematics were determined during flexion from full extension to 120 degrees for ten normal, healthy, subjects using fluoroscopy and a 3D-to-2D registration method. All ten subjects had previously undergone CT scans allowing for the digital reconstruction of native femur and tibia geometries. These geometries were then input into a ridged body forward model based on Kane's system of dynamics. The resulting kinematics determined through fluoroscopy and the mathematical model were compared for all of the ten subjects.Background
Methods
Historically, knee implants have been designed using average patient anatomy and despite excellent implant survivorship, patient satisfaction is not consistently achieved. One possibility for this dissatisfaction relates to the individual patient anatomic variability. To reduce this inter-patient variability, recent advances in imaging and manufacturing have allowed for the implementation of patient specific posterior cruciate retaining (PCR) total knee arthroplasty (TKA). These implants are individually made based on a patient's femoral and tibial anatomy determined from a pre-operative CT scan. Although in-vitro studies have demonstrated promising results, there are few studies evaluating these implants in vivo. The objective of this study was to determine the in vivo kinematics for subjects having a customized, individually made(CIM) knee implant or one of several traditional, off-the-shelf (OTS) TKA designs. In vivo kinematics were assessed for 108 subjects, 44 having a CIM-PCR-TKA and 64 having one of three standard designs, OTS-PCR-TKA which included symmetric TKA(I), single radius TKA(II) and asymmetric TKA(III) designs. A mobile fluoroscopic system was used to observe subjects during a weight-bearing deep knee bend (DKB), a Chair Rise and Normal Gait. All the subjects were implanted by one of two surgeons and were clinically successful (HSS Score>90). The kinematic comparison between the three designs involved range of motion, femoral translation, axial rotation, and condylar lift-off.Introduction
Methods
Currently, knee and hip implants are evaluated experimentally using mechanical simulators or clinically using long-term follow-up. Unfortunately, it is not practical to mechanically evaluate all patient and surgical variables and predict the viability of implant success and/or performance. More recently, a validated mathematical model has been developed that can theoretically simulate new implant designs under in vivo conditions to predict joint forces kinematics and performance. Therefore, the objective of this study was to use a validated forward solution model (FSM) to evaluate new and existing implant designs, predicting mechanics of the hip and knee joints. The model simulates the four quadriceps muscles, the complete hamstring muscle group, all three gluteus muscles, iliopsoas group, tensor fasciae latae, and an adductor muscle group. Other soft tissues include the patellar ligament, MCL, LCL, PCL, ACL, multiple ligaments connecting the patella to the femur, and the primary hip capsular ligaments (ischiofemoral, iliofemoral, and pubofemoral). The model was previously validated using telemetric implants and fluoroscopic results and is now being used to analyze multiple implant geometries. Virtual implantation allows for various surgical alignments to determine the effect of surgical errors. Furthermore, the model can simulate resecting, weakening, or tightening of soft tissues based on surgical errors or technique modifications.Introduction
Methods
In-vivo data pertaining to the actual cam-post engagement mechanism in PS and Bi-Cruciate Stabilized (BCS) knees is still very limited. Therefore, the objective of this study was to determine the cam-post mechanism interaction under in-vivo, weight-bearing conditions for subjects implanted with either a Rotating Platform (RP) PS TKA, a Fixed Bearing (FB) PS TKA or a FB BCS TKA. In-vivo, weight-bearing, 3D knee kinematics were determined for eight subjects (9 knees) having a RP-PS TKA (DePuy Inc.), four subjects (4 knees) with FB-PS TKA (Zimmer Inc.), and eight subjects (10 knees) having BCS TKA (Smith&Nephew Inc.), while performing a deep knee bend. 3D-kinematics was recreated from fluoroscopic images using a previously published 3D-to-2D registration technique (Figure 1). Images from full extension to maximum flexion were analyzed at 10° intervals. Once the 3D-kinematics of implant components was recreated, the cam-post mechanism was scrutinized. The distance between the interacting surfaces was monitored throughout flexion and the predicted contact map was calculated.INTRODUCTION
METHODS
Orthopaedic companies spend years and millions of dollars developing and verifying new total knee arthroplasty (TKA) designs. Recently, computational models have been used in the hopes of increasing the efficiency of the design process. The most popular predictive models simulate a cadaveric rig. Simulations of these rigs, although useful, do not predict in vivo behavior. Therefore, in this current study, the development of a physiological forward solution, or predictive, rigid body model of the knee is described. The models simulate a non-weight bearing extension activity or a weight-bearing deep knee bend (DKB) activity. They solve for both joint forces and kinematics simultaneously and were developed from the ground up. The models are rigid body and use Kane's dynamical equations. The model began with a simple two dimensional non-weight bearing extension activity model of the tibiofemoral joint. Step by step the model was expanded. Quadriceps and hamstring muscles were added to drive the motion. Ligaments were added represented by multiple non-linear spring elements. The model was expanded to three-dimensions (3D) allowing out of plane motions and calculation of medial and lateral condylar forces. The patella was added as its own body allowing for simulation of the patellofemoral joint. The model was then converted to a weight bearing deep knee bend activity. A pelvis and trunk were added and muscles were given physiological origin and insertion points. A modified proportional-integral-derivative (PID) controller was implemented to control the rate of flexion and also to assist in joint stability by adjusting the force in individual quadriceps muscles. A method for representing articulating geometry was developed. Once the deep knee bend model was fully developed (Figure 1) it was converted back to a non-weight bearing extension model (Figure 2) resulting in simulations of a normal knee performing a weight bearing and non-weight bearing activity. The tibiofemoral kinematic results were compared to in vivo kinematics obtained from a fluoroscopy study of five normal subjects. Parameters from the CT models of one of these subjects (Subject 3) were used in the model. The model kinematics behave as the normal knee does in vivo. The kinetic results were within reasonable ranges with a maximum total quadriceps force of 0.86 BW and 4.73 BW for extension and DKB simulations, respectively (Figure 3 and Figure 4). The maximum total tibiofemoral forces were 1.26 BW and 3.70 BW for extension and DKB, respectively. The relationship between the quadriceps force, patella ligament force and patellofemoral forces are consistent with how the extensor mechanism behaves (Figure 3 and Figure 4). The patellofemoral forces are low between 0 and 20 degrees flexion and the patella ligament and quadriceps forces are close in magnitude from 0 to around 70 degrees flexion when the patellofemoral forces increase and the quadriceps forces increase relative to the patella ligament force. The model allows for virtual implantation of TKA geometry and after kinematic and kinetic validation from in vivo TKA data can be used to predict the behavior of TKA in vivo.
Total shoulder arthroplasty (TSA) implants are used to restore function to individuals whose shoulder motions are impaired by osteoarthritis. To improve TSA implant designs, it is crucial to understand the kinematics of healthy, osteoarthritic (OA), and post-TSA shoulders. Hence, this study will determine in vivo kinematic trends of the glenohumeral joints of healthy, OA, and post-TSA shoulders. In vivo shoulder kinematics were determined pre and post-operatively for five unilateral TSA subjects with one healthy and a contralateral OA glenohumeral joint. Fluoroscopic examinations were performed for all three shoulder categories (healthy, OA, and post-TSA) for each subject shoulder abduction and external rotation. Then, three-dimensional (3D) models of the left and right scapula and humerus were constructed using CT scans. For post-operative shoulders, 3D computer-aided design models of the implants were obtained. Next, the 3D glenohumeral joint kinematics were determined using a previously published 3D to 2D registration technique. After determining kinematics, relative Euler rotation angles between the humerus and scapula were calculated in MATLAB® to determine range of motion (ROM) and kinematic profiles for all three shoulder categories. The ROMs for each category were compared using paired t-tests for each exercise. Also, the location of the contact point of the humerus on the glenoid was found. This allowed the vertical translation from the most superior to most inferior contact point (SI contact range) to be calculated as well as the horizontal translation from the most anterior to most posterior contact point (AP contact range). The SI and AP contact ranges for all shoulder categories were compared using paired t-tests for each exercise.INTRODUCTION
Methods
