Dynamic finite element knee simulation for evaluation of knee replacement mechanics

Baldwin, Mark A.; Clary, Chadd W.; Fitzpatrick, Clare K.; Deacy, James S.; Maletsky, Lorin P.; Rullkoetter, Paul J.

doi:10.1016/j.jbiomech.2011.11.052

Cited by 147 publications

(116 citation statements)

References 48 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…Static forces at discrete flexion angles have been applied to finite element models to determine component stress [6]. In addition prosthetic wear simulators [20] and experimental joint simulators [16,21,22] have been used to provide dynamic joint boundaries and loading. Zelle et al recently simulated a weight-bearing squatting motion by applying ground reaction forces to the distal tibia and incrementally releasing a constrained quadriceps tendon to achieve knee flexion [23].…”

Section: Introductionmentioning

confidence: 99%

Multibody Muscle Driven Model of an Instrumented Prosthetic Knee During Squat and Toe Rise Motions

Stylianou¹,

Guess²,

Kia

2013

Journal of Biomechanical Engineering

View full text Add to dashboard Cite

Detailed knowledge of knee joint kinematics and dynamic loading is essential for improving the design and outcomes of surgical procedures, tissue engineering applications, prosthetics design, and rehabilitation. The need for dynamic computational models that link kinematics, muscle and ligament forces, and joint contacts has long been recognized but such body-level forward dynamic models do not exist in recent literature. A main barrier in using computational models in the clinic is the validation of the in vivo contact, muscle, and ligament loads. The purpose of this study was to develop a full body, muscle driven dynamic model with subject specific leg geometries and validate it during squat and toe-rise motions. The model predicted loads were compared to in vivo measurements acquired with an instrumented knee implant. Data for this study were provided by the "Grand Challenge Competition to Predict In-Vivo Knee Loads" for the 2012 American Society of Mechanical Engineers Summer Bioengineering Conference. Data included implant and bone geometries, ground reaction forces, EMG, and the instrumented knee implant measurements. The subject specific model was developed in the multibody framework. The knee model included three ligament bundles for the lateral collateral ligament (LCL) and the medial collateral ligament (MCL), and one bundle for the posterior cruciate ligament (PCL). The implanted tibia tray was segmented into 326 hexahedral elements and deformable contacts were defined between the elements and the femoral component. The model also included 45 muscles on each leg. Muscle forces were computed for the muscle driven simulation by a feedback controller that used the error between the current muscle length in the forward simulation and the muscle length recorded during a kinematics driven inverse simulation. The predicted tibia forces and torques, ground reaction forces, electromyography (EMG) patterns, and kinematics were compared to the experimentally measured values to validate the model. Comparisons were done graphically and by calculating the mean average deviation (MAD) and root mean squared deviation (RMSD) for all outcomes. The MAD value for the tibia vertical force was 279 N for the squat motion and 325 N for the toe-rise motion, 45 N and 53 N for left and right foot ground reaction forces during the squat and 94 N and 82 N for toe-rise motion. The maximum MAD value for any of the kinematic outcomes was 7.5 deg for knee flexion-extension during the toe-rise motion.

show abstract

Section: Introductionmentioning

confidence: 99%

Multibody Muscle Driven Model of an Instrumented Prosthetic Knee During Squat and Toe Rise Motions

Stylianou¹,

Guess²,

Kia

2013

Journal of Biomechanical Engineering

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the rapid prototyped implants in comparison to the original reference implant three different aspects have been investigated: The surface accuracy (1), surface roughness (2) and the resulting kinematics (3). (1) The surface accuracy of the femoral components was determined by an optical 3D Scanner (HandySCAN 700, Creaform, Levis, Canada), which has a resolution of 0.2 mm.…”

Section: Methodsmentioning

confidence: 99%

“…prototype implants can be in vitro evaluated. Problematic is the expensive and time consuming manufacturing process of these new implants, when multiple designs have to be evaluated [3]. Since there are different requirements for in vitro tests than for in vivo implantation, especially in terms of durability and biocompatibility, rapid prototyping technologies might have the potential to offer an easy and fast access to various implant designs [4] and [5] investigated the use of rapid prototyping for in vitro knee experiments and concluded that replica prostheses manufactured by PolyJetModelling can reproduce the original implant kinematics, although higher friction levels under low axial loads have been determined.…”

Section: Introductionmentioning

confidence: 99%

Rapid prototyping of replica knee implants for in vitro testing

Verjans

Asseln

Radermacher

2016

Current Directions in Biomedical Engineering

View full text Add to dashboard Cite

Abstract:The understanding of the complex biomechanics of the knee is a key for an optimal implant design. To easily investigate the influence of prosthetic designs on knee biomechanics a rapid prototyping workflow for knee implants has been developed and evaluated. Therefore, different manufacturing technologies and post-treatment methods have been examined and overall seven different replica knee implants were manufactured. For evaluation, the manufacturing properties such as surface accuracy and roughness were determined and kinematic behaviour was investigated in a novel knee testing rig. It was carried out that PolyJet-Modelling with a sanded surface resulted in changed kinematic patterns compared to a usual CoCr-UHMWPE implant. However, fused deposition modelling using ABS and subsequent surface smoothening with acetone vapor showed the lowest roughness of the manufactured implants and only minor kinematic differences. For this reason this method constitutes a promising approach towards an optimal implant design for improved patientsatisfaction and long lifetime of the implant. Finally the workflow is not only limited to the knee.

show abstract

“…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. However, the data and method used to collect the knee laxity in these experiments can be difficult to attain in a clinical setting because of cost and time implication.…”

Section: Discussionmentioning

confidence: 99%

“…From this non-functional imaging, subject specific bony morphology and anatomical landmarks can be obtained but not the ligament characteristics. There have been few studies that able to estimate ligament soft tissue properties of cadavers from the knee laxity data recorded experimentally (Ewing et al, 2016;Baldwin et al, 2012;Bloemeker et al, 2012). However, the data and method used to collect the knee laxity in these experiments can be difficult to attain in a clinical setting because of cost and time implication.…”

Section: Introductionmentioning

confidence: 99%

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

Theodore¹,

Little²,

Liu³

et al.

EPiC Series in Health Sciences

View full text Add to dashboard Cite

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 characteristics 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 suggest the ligaments characteristics vary widely between subjects and non-functional imaging is insufficient to determine its characteristics. These large variations necessitates a subject-specific approach when creating knee computational models and functional radiographs may be a viable method to characterise patient specific ligaments.

show abstract

Dynamic finite element knee simulation for evaluation of knee replacement mechanics

Cited by 147 publications

References 48 publications

Multibody Muscle Driven Model of an Instrumented Prosthetic Knee During Squat and Toe Rise Motions

Multibody Muscle Driven Model of an Instrumented Prosthetic Knee During Squat and Toe Rise Motions

Rapid prototyping of replica knee implants for in vitro testing

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

Contact Info

Product

Resources

About