Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials

Predicting Knee Joint Contact Forces During Normal Walking Using Kinematic Inputs With a Long-Short Term Neural Network

Knee joint contact forces are commonly estimated via surrogate measures (i.e., external knee adduction moments or musculoskeletal modeling). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience. The purpose of this study was to design a novel prediction method for knee joint contact forces that is simplistic in terms of required inputs. This study included marker trajectories and instrumented knee forces during normal walking from the "Grand Challenge" (n = 6) and "CAMS" (n = 2) datasets. Inverse kinematics were used to derive stance phase hip (sagittal, frontal, transverse), knee (sagittal, frontal), ankle (sagittal), and trunk (frontal) kinematics. A long-short term memory network (LSTM) was created using matlab to predict medial and lateral knee force waveforms using combinations of the kinematics. The Grand Challenge and CAMS datasets trained and tested the network, respectively. Musculoskeletal modeling forces were derived using static optimization and joint reaction tools in OpenSim. Waveform accuracy was determined as the proportion of variance and root-mean-square error between network predictions and in vivo data. The LSTM network was highly accurate for medial forces (R2 = 0.77, RMSE = 0.27 BW) and required only frontal hip and knee and sagittal hip and ankle kinematics. Modeled medial force predictions were excellent (R2 = 0.77, RMSE = 0.33 BW). Lateral force predictions were poor for both methods (LSTM R2 = 0.18, RMSE = 0.08 BW; modeling R2 = 0.21, RMSE = 0.54 BW). The designed LSTM network outperformed most reports of musculoskeletal modeling, including those reached in this study, revealing knee joint forces can accurately be predicted by using only kinematic input variables.

Higher coactivations of lower limb muscles increase stability during walking on slippery ground in forward dynamics musculoskeletal simulation

The energy efficiency theory of human bipedal locomotion has been widely accepted as a neuro-musculoskeletal control method. However, coactivation of agonist and antagonist muscles in the lower limb has been observed during various limb movements, including walking. The emergence of this coactivation cannot be explained solely by the energy efficiency theory and remains a subject of debate. To shed light on this, we investigated the role of muscle coactivations in walking stability using a forward dynamics musculoskeletal simulation combined with neural-network-based gait controllers. Our study revealed that a gait controller with minimal muscle activations had a high probability of falls under challenging gait conditions such as slippery ground and uneven terrain. Lower limb muscle coactivations emerged in the process of gait controller training on slippery ground. Controllers with physiological coactivation levels demonstrated a significantly reduced probability of falls. Our results suggest that achieving stable walking requires muscle coactivations beyond the minimal level of muscle energy. This study implies that coactivations likely emerge to maintain gait stability under challenging conditions, and both coactivation and energy optimization of lower limb muscles should be considered when exploring the foundational control mechanisms of human walking.

A novel method to accurately recreate in vivo loads and kinematics in computational models of the knee

Despite availability of in vivo knee loads and kinematics data, conventional load- and displacement-controlled configurations still can't accurately predict tibiofemoral loads from kinematics or vice versa. We propose a combined load- and displacement-control method for joint-level simulations of the knee to reliably reproduce in vivo contact mechanics. Prediction errors of the new approach were compared to those of conventional purely load- or displacement-controlled models using in vivo implant loads and kinematics for multiple subjects and activities (CAMS-Knee dataset). Our method reproduced both loads and kinematics more closely than conventional models and thus demonstrates clear advantages for investigating tibiofemoral contact or wear.

Can a Musculoskeletal Model Adapted to Knee Implant Geometry Improve Prediction of 3D Contact Forces and Moments?

Tibiofemoral contact loads are crucial parameters in the onset and progression of osteoarthrosis. While contact loads are frequently estimated from musculoskeletal models, their customization is often limited to scaling musculoskeletal geometry or adapting muscle lines. Moreover, studies have usually focused on superior-inferior contact force without investigating three-dimensional contact loads. Using experimental data from six patients with instrumented total knee arthroplasty (TKA), this study customized a lower limb musculoskeletal model to consider the positioning and the geometry of the implant at knee level. Static optimization was performed to estimate tibiofemoral contact forces and contact moments as well as musculotendinous forces. Predictions from both a generic and a customized model were compared to the instrumented implant measurements. Both models accurately predict superior-inferior (SI) force and abduction-adduction (AA) moment. Notably, the customization improves prediction of medial-lateral (ML) force and flexion-extension (FE) moments. However, there is subject-dependent variability in the prediction of anterior-posterior (AP) force. The customized models presented here predict loads on all joint axes and in most cases improve prediction. Unexpectedly, this improvement was more limited for patients with more rotated implants, suggesting a need for further model adaptations such as muscle wrapping or redefinition of hip and ankle joint centers and axes.

The optimal design of 3D-printed lattice bone plate by considering fracture healing mechanism

The biomechanical stimulus is the most important factor for fracture healing and mainly determined by the structural stiffness of bone plate. Currently, the materials commonly used in bone plates are stainless steel and titanium, which often lead to stress shielding effects because of their higher elastic modulus compared with the bone. This article suggests an optimal design method of lattice bone plate based on fracture healing theory. First, the mechanical regulation model with deviatoric strain is established to simulate the tissue differentiation process during fracture healing process. The ratio of the average elastic modulus of callus at the 120th day to the elastic modulus of mature bone is used to characterize the fracture healing rate. Second, the optimal elastic modulus of the design domain is obtained by the optimization mathematical model with the maximum fracture healing rate. Then, the design domain is filled with microstructures, the porosity of which is adjusted to make it possible that the equivalent elastic modulus is equal to the optimized value. And the finite element analysis of the bone plate with microstructure is executed. Finally, the designed lattice bone plates are manufactured through 3D printing, and the mechanical test is carried out. The simulation results indicate that the fracture healing rate is maximum when the elastic modulus of material in design domain is 38 GPa under the constraints of fixation stability. And both the finite element analysis and experiment results show that the designed lattice bone plate meet the strength requirements of fracture internal fixation implants.

[Musculoskeletal multibody dynamics investigation of posterior-stabilized total knee prosthesis]

Posterior-stabilized total knee prostheses have been widely used in orthopedic clinical treatment of knee osteoarthritis, but the patients and surgeons are still troubled by the complications, for example severe wear and fracture of the post, as well as prosthetic loosening. Understanding the in vivo biomechanics of knee prostheses will aid in the decrease of postoperative prosthetic revision and patient dissatisfaction. Therefore, six different designs of posterior-stabilized total knee prostheses were used to establish the musculoskeletal multibody dynamics models of total knee arthroplasty respectively, and the biomechanical differences of six posterior-stabilized total knee prostheses were investigated under three simulated physiological activities: walking, right turn and squatting. The results showed that the post contact forces of PFC Sigma and Scorpio NGR prostheses were larger during walking, turning right, and squatting, which may increase the risk of the fracture and wear as well as the early loosening. The post design of Gemini SL prosthesis was more conductive to the knee internal-external rotation and avoided the edge contact and wear. The lower conformity design in sagittal plane and the later post-cam engagement resulted in the larger anterior-posterior translation. This study provides a theoretical support for guiding surgeon selection, improving posterior-stabilized prosthetic design and reducing the prosthetic failure.

A subject-specific musculoskeletal model to predict the tibiofemoral contact forces during daily living activities

Accurate prediction of tibiofemoral contact force (TFCF) during daily living activities is significant for understanding the initiation, progression, and treatment of knee osteoarthritis (KOA). However, the diversity of target activities, prediction accuracy, and computational efficiency of the current musculoskeletal simulations need to be further improved. In this study, a subject-specific musculoskeletal model considered the tibiofemoral alignment, medial-lateral contact locations, secondary tibiofemoral and all patellofemoral motions, and knee ligaments was proposed to predict the TFCFs during the five daily activities (normal walking, sit-to-stand, stand-to-sit, stair ascent, and stair descent) in OpenSim software. The standing lower-limbs-full-length radiograph, local radiograph of knee joint, motion capture data, and force plate data of eighteen subjects were acquired as the input data of the musculoskeletal model. The results showed good agreements of TFCFs between the predictions based on our proposed musculoskeletal model and the in-vivo measurements based on instrumented knee implants during the five daily activities (RMSE: 0.16 ∼ 0.31 BW, R²: 0.88 ∼ 0.97, M: -0.11 ∼ -0.02, P: 0.03 ∼ 0.10, and C: 0.04 ∼ 0.14). Additionally, the order of the peak total and lateral TFCFs from low to high was normal walking, stair ascent and stand-to-sit, and stair descent and sit-to-stand (P < 0.05), and the peak medial TFCF was stand-to-sit, sit-to-stand, normal walking, stair ascent and stair descent (P < 0.05). The outcomes of this study are valuable for further understanding the knee biomechanics during daily living activities and providing theoretical guidance for the treatments of KOA.

Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.

Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction

Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.

Chronic Ankle Instability Does Not Influence Tibiofemoral Contact Forces During Drop Landings Using a Musculoskeletal Model

The purpose of the study was to compare the tibiofemoral contact forces of participants with chronic ankle instability versus controls during landings using a computer-simulated musculoskeletal model. A total of 21 female participants with chronic ankle instability and 21 pair-matched controls performed a drop landing task on a tilted force plate. A 7-camera motion capture system and 2 force plates were used to test participants' lower-extremity biomechanics. A musculoskeletal model was used to calculate the tibiofemoral contact forces (femur on tibia). No significant between-group differences were observed for the peak tibiofemoral contact forces (P = .25-.48) during the landing phase based on paired t tests. The group differences ranged from 0.05 to 0.58 body weight (BW). Most participants demonstrated a posterior force (peak, ∼1.1 BW) for most duration of the landing phase and a medial force (peak, ∼0.9 BW) and large compressive force (peak, ∼10 BW) in the landing phase. The authors conclude that chronic ankle instability may not be related to the increased tibiofemoral contact forces or knee injury mechanisms during landings on the tilted surface.

Influences of load carriage and physical activity history on tibia bone strain

BACKGROUND

Military recruits are often afflicted with stress fractures. The military's strenuous training programs involving load carriage may contribute to the high incidence of tibia stress fractures in the army. The purpose of this study was to assess the influences of incremented load carriage and history of physical activity on tibia bone strain and strain rate during walking.

METHODS

Twenty recreational basketball players and 20 recreational runners performed 4 walking tasks while carrying 0 kg, 15 kg, 25 kg, and 35 kg loads, respectively. Tibia bone strain and strain rate were obtained through subject-specific multibody dynamic simulations and finite element analyses. Mixed model repeated-measures analyses of variance were conducted.

RESULTS

The mean ± SE of the runners' bone strain (μs) during load carriages (0 kg, 15 kg, 25 kg, and 35 kg) were 658.11 ± 1.61, 804.41 ± 1.96, 924.49 ± 2.23, and 1011.15 ± 2.71, respectively, in compression and 458.33 ± 1.45, 562.11 ± 1.81, 669.82 ± 2.05, and 733.40 ± 2.52, respectively, in tension. For the basketball players, the incremented load carriages resulted in compressive strain of 634.30 ± 1.56, 746.87 ± 1.90, 842.18 ± 2.16, and 958.24 ± 2.63, respectively, and tensile strain of 440.04 ± 1.41, 518.86 ± 1.75, 597.63 ± 1.99, and 700.15 ± 2.47, respectively. A dose-response relationship exists between incremented load carriage and bone strain and strain rate. A history of regular basketball activity could result in reduced bone strain and reduced strain rate.

CONCLUSION

Load carriage is a risk factor for tibia stress fracture during basic training. Preventative exercise programs, such as basketball, that involved multidirectional mechanical loading to the tibia bones can be implemented for military recruits before basic training commences.

Finite element analysis of knee and ankle joint during gait based on motion analysis

Contact pressures in the articular cartilage during gait affect injuries and the degenerative arthritis of knee and ankle joints. However, only contact forces at the knee and ankle joints during gait can be estimated by using a rigid body dynamic model. The contact pressure distribution can be obtained quantitatively for a static posture by using finite element (FE) analysis in most cases. The purpose of this study is to develop a new method to obtain the contact pressure distribution at the knee and ankle joints during gait by integrating FE analysis with rigid body dynamic analysis. In this method, a reference FE model of the lower extremity is constructed first and is then transformed to each stance phase of the gait obtained from dynamic analysis by using homogeneous transformation. The muscle forces and ground reaction force (GRF) during gait obtained from the dynamic analysis were used as loading conditions for FE analysis. Finally, the contact pressure distribution at the tibia plateau cartilage and talus cartilage were estimated at the 1st peak, mid-stance, and the 2nd peak at the same time. The present method can provide the contact pressure distribution at the knee and ankle joints over the entire gait.

Electromyography-Driven Forward Dynamics Simulation to Estimate In Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gaits

Computational models that predict in vivo joint loading and muscle forces can potentially enhance and augment our knowledge of both typical and pathological gaits. To adopt such models into clinical applications, studies validating modeling predictions are essential. This study created a full-body musculoskeletal model using data from the "Sixth Grand Challenge Competition to Predict in vivo Knee Loads." This model incorporates subject-specific geometries of the right leg in order to concurrently predict knee contact forces, ligament forces, muscle forces, and ground contact forces. The objectives of this paper are twofold: (1) to describe an electromyography (EMG)-driven modeling methodology to predict knee contact forces and (2) to validate model predictions by evaluating the model predictions against known values for a patient with an instrumented total knee replacement (TKR) for three distinctly different gait styles (normal, smooth, and bouncy gaits). The model integrates a subject-specific knee model onto a previously validated generic full-body musculoskeletal model. The combined model included six degrees-of-freedom (6DOF) patellofemoral and tibiofemoral joints, ligament forces, and deformable contact forces with viscous damping. The foot/shoe/floor interactions were modeled by incorporating shoe geometries to the feet. Contact between shoe segments and the floor surface was used to constrain the shoe segments. A novel EMG-driven feedforward with feedback trim motor control strategy was used to concurrently estimate muscle forces and knee contact forces from standard motion capture data collected on the individual subject. The predicted medial, lateral, and total tibiofemoral forces represented the overall measured magnitude and temporal patterns with good root-mean-squared errors (RMSEs) and Pearson's correlation (p2). The model accuracy was high: medial, lateral, and total tibiofemoral contact force RMSEs = 0.15, 0.14, 0.21 body weight (BW), and (0.92 < p2 < 0.96) for normal gait; RMSEs = 0.18 BW, 0.21 BW, 0.29 BW, and (0.81 < p2 < 0.93) for smooth gait; and RMSEs = 0.21 BW, 0.22 BW, 0.33 BW, and (0.86 < p2 < 0.95) for bouncy gait, respectively. Overall, the model captured the general shape, magnitude, and temporal patterns of the contact force profiles accurately. Potential applications of this proposed model include predictive biomechanics simulations, design of TKR components, soft tissue balancing, and surgical simulation.

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

A dynamic musculoskeletal model of the elbow joint in which muscle, ligament, and articular surface contact forces are predicted concurrently would be an ideal tool for patient-specific preoperative planning, computer-aided surgery, and rehabilitation. Existing musculoskeletal elbow joint models have limited clinical applicability because of idealizing the elbow as a mechanical hinge joint or ignoring important soft tissue (e.g., cartilage) contributions. The purpose of this study was to develop a subject-specific anatomically correct musculoskeletal elbow joint model and evaluate it based on experimental kinematics and muscle electromyography measurements. The model included three-dimensional bone geometries, a joint constrained by multiple ligament bundles, deformable contacts, and the natural oblique wrapping of ligaments. The musculoskeletal model predicted the bone kinematics reasonably accurately in three different velocity conditions. The model predicted timing and number of muscle excitations, and the normalized muscle forces were also in agreement with the experiment. The model was able to predict important in vivo parameters that are not possible to measure experimentally, such as muscle and ligament forces, and cartilage contact pressure. In addition, the developed musculoskeletal model was computationally efficient for body-level dynamic simulation. The maximum computation time was less than 30 min for our 35 s simulation. As a predictive clinical tool, the potential medical applications for this model and modeling approach are significant.

Knee medial and lateral contact forces in a musculoskeletal model with subject-specific contact point trajectories

Contact point (CP) trajectory is a crucial parameter in estimating medial/lateral tibio-femoral contact forces from the musculoskeletal (MSK) models. The objective of the present study was to develop a method to incorporate the subject-specific CP trajectories into the MSK model. Ten healthy subjects performed 45 s treadmill gait trials. The subject-specific CP trajectories were constructed on the tibia and femur as a function of extension-flexion using low-dose bi-plane X-ray images during a quasi-static squat. At each extension-flexion position, the tibia and femur CPs were superimposed in the three directions on the medial side, and in the anterior-posterior and proximal-distal directions on the lateral side to form the five kinematic constraints of the knee joint. The Lagrange multipliers associated to these constraints directly yielded the medial/lateral contact forces. The results from the personalized CP trajectory model were compared against the linear CP trajectory and sphere-on-plane CP trajectory models which were adapted from the commonly used MSK models. Changing the CP trajectory had a remarkable impact on the knee kinematics and changed the medial and lateral contact forces by 1.03 BW and 0.65 BW respectively, in certain subjects. The direction and magnitude of the medial/lateral contact force were highly variable among the subjects and the medial-lateral shift of the CPs alone could not determine the increase/decrease pattern of the contact forces. The suggested kinematic constraints are adaptable to the CP trajectories derived from a variety of joint models and those experimentally measured from the 3D imaging techniques.

Computer simulation of lumbar flexion shows shear of the facet capsular ligament

BACKGROUND CONTEXT

The lumbar facet capsular ligament (FCL) is a posterior spinal ligament with a complex structure and kinematic profile. The FCL has a curved geometry, multiple attachment sites, and preferentially aligned collagen fiber bundles on the posterior surface that are innervated with mechanoreceptive nerve endings. Spinal flexion induces three-dimensional (3D) deformations, requiring the FCL to maintain significant tensile and shear loads. Previous works aimed to study 3D facet joint kinematics during flexion, but to our knowledge none have reported localized FCL surface deformations likely created by this complex structure.

PURPOSE

The purpose of this study was to elucidate local deformations of both the posterior and anterior surfaces of the lumbar FCL to understand the distribution and magnitude of in-plane and through-plane deformations, including the prevalence of shear.

STUDY DESIGN/SETTING

The FCL anterior and posterior surface deformations were quantified through creation of a finite element model simulating facet joint flexion using a realistic geometry, physiological kinematics, and fitted constitutive material.

METHODS

Geometry was obtained from the micro-CT data of a healthy L3-L4 facet joint capsule (n=1); kinematics were extracted from sagittal plane fluoroscopic data of healthy volunteers (n=10) performing flexion; and average material properties were determined from planar biaxial extension tests of L4-L5 FCLs (n=6). All analyses were performed with the non-linear finite element solver, FEBio. A grid of equally spaced 3×3 nodes on the posterior surface identified regional differences within the strain fields and was used to create comparisons against previously published experimental data. This study was funded by the National Institutes of Health and the authors have no disclosures.

RESULTS

Inhomogeneous in-plane and through-plane shear deformations were prominent through the middle body of the FCL on both surfaces. Anterior surface deformations were more pronounced because of the small width of the joint space, whereas posterior surface deformations were more diffuse because the larger area increased deformability. We speculate these areas of large deformation may provide this proprioceptive system with an excellent measure of spinal motion.

CONCLUSIONS

We found that in-plane and through-plane shear deformations are widely present in finite element simulations of a lumbar FCL during flexion. Importantly, we conclude that future studies of the FCL must consider the effects of both shear and tensile deformations.

A Multibody Knee Model Corroborates Subject-Specific Experimental Measurements of Low Ligament Forces and Kinematic Coupling During Passive Flexion

A multibody model of the knee was developed and the predicted ligament forces and kinematics during passive flexion corroborated subject-specific measurements obtained from a human cadaveric knee that was tested using a robotic manipulator. The model incorporated a novel strategy to estimate the slack length of ligament fibers based on experimentally measured ligament forces at full extension and included multifiber representations for the cruciates. The model captured experimentally measured ligament forces (≤ 5.7 N root mean square (RMS) difference), coupled internal rotation (≤ 1.6 deg RMS difference), and coupled anterior translation (≤ 0.4 mm RMS difference) through 130 deg of passive flexion. This integrated framework of model and experiment improves our understanding of how passive structures, such as ligaments and articular geometries, interact to generate knee kinematics and ligament forces.

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.

How accurate and reproducible are the identification of cruciate and collateral ligament insertions using MRI?

BACKGROUND

At present, increasing the accuracy of identification of knee ligament insertions is fundamental in developing accurate patient-specific three-dimensional (3D) models for preoperative planning surgeries, designing patient-specific instrumentation or implants, and conducting biomechanical analyses. The accuracy and reproducibility of magnetic resonance imaging (MRI) scans in identifying cruciate and collateral ligament insertions have not been investigated thus far, despite their wide use. This study aimed to define and validate a method for this purpose.

METHODS

First, the femur and tibia bones were oriented. Then, the sites of cruciate and collateral ligament insertions were identified. The inter- and intra-class correlation coefficients (ICCs) were calculated after multiple operators were applied to the images of different patients. The effect of the quality of the available MRI scans on the accuracy of identifying ligament insertions was also investigated. Moreover, non-expert operators were also found to be capable of performing the procedure after a video tutorial, and its efficacy was tested.

RESULTS

The mean intra-observer variability was always <1.5mm for all landmarks, whereas the mean inter-observer variability was always <2.5mm except for the medial collateral ligament localized on the tibia (6.7mm). The ICCs showed good results, up to 0.99. A lower image quality had no noticeable effect on the procedure for identifying cruciate ligaments. Video tutorials were found to improve the ICCs up to 45%.

CONCLUSIONS

This procedure is suitable for the univocal and accurate identification of cruciate and lateral collateral ligaments on MRI scans.

CLINICAL RELEVANCE

This study demonstrates that MRI scans are highly suited to identifying cruciate ligament insertions.

Muscle parameters estimation based on biplanar radiography

The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography.

Prediction of medial and lateral contact force of the knee joint during normal and turning gait after total knee replacement

The computational modeling approach has commonly been used to predict knee joint contact forces, muscle forces, and ligament loads during activities of daily living. Knowledge of these forces has several potential applications, for example, within design of equipment to protect the knee joint from injury and to plan adequate rehabilitation protocols, although clinical applications of computational models are still evolving and one of the limiting factors is model validation. The objective of this study was to extend previous modeling technique and to improve the validity of the model prediction using publicly available data set of the fifth "Grand Challenge Competition to Predict In Vivo Knee Loads." A two-stage modeling approach, which combines conventional inverse dynamic analysis (the first stage) with a multi-body subject-specific lower limb model (the second stage), was used to calculate medial and lateral compartment contact forces. The validation was performed by direct comparison of model predictions and experimental measurement of medial and lateral compartment contact forces during normal and turning gait. The model predictions of both medial and lateral contact forces showed strong correlations with experimental measurements in normal gait (r = 0.75 and 0.71) and in turning gait trials (r = 0.86 and 0.72), even though the current technique over-estimated medial compartment contact forces in swing phase. The correlation coefficient, Sprague and Geers metrics, and root mean squared error indicated that the lateral contact forces were predicted better than medial contact forces in comparison with the experimental measurements during both normal and turning gait trials.

Intra-Articular Knee Contact Force Estimation During Walking Using Force-Reaction Elements and Subject-Specific Joint Model2

Joint contact forces measured with instrumented knee implants have not only revealed general patterns of joint loading but also showed individual variations that could be due to differences in anatomy and joint kinematics. Musculoskeletal human models for dynamic simulation have been utilized to understand body kinetics including joint moments, muscle tension, and knee contact forces. The objectives of this study were to develop a knee contact model which can predict knee contact forces using an inverse dynamics-based optimization solver and to investigate the effect of joint constraints on knee contact force prediction. A knee contact model was developed to include 32 reaction force elements on the surface of a tibial insert of a total knee replacement (TKR), which was embedded in a full-body musculoskeletal model. Various external measurements including motion data and external force data during walking trials of a subject with an instrumented knee implant were provided from the Sixth Grand Challenge Competition to Predict in vivo Knee Loads. Knee contact forces in the medial and lateral portions of the instrumented knee implant were also provided for the same walking trials. A knee contact model with a hinge joint and normal alignment could predict knee contact forces with root mean square errors (RMSEs) of 165 N and 288 N for the medial and lateral portions of the knee, respectively, and coefficients of determination (R2) of 0.70 and −0.63. When the degrees-of-freedom (DOF) of the knee and locations of leg markers were adjusted to account for the valgus lower-limb alignment of the subject, RMSE values improved to 144 N and 179 N, and R2 values improved to 0.77 and 0.37, respectively. The proposed knee contact model with subject-specific joint model could predict in vivo knee contact forces with reasonable accuracy. This model may contribute to the development and improvement of knee arthroplasty.

Predicted loading on the menisci during gait: The effect of horn laxity

Radiographic measurements have established a link between meniscus extrusion and meniscus degeneration as well as with knee osteoarthritis. The presented work combines medical imaging with motion capture data from two healthy female subjects to create subject specific knee models that predict tibio-menisco-femoral contact forces and ligament forces during muscle driven simulations of barefoot gait. The developed computational models were used to explore the relationship between the extent of meniscal extrusion and biomechanical function by altering the laxity of the meniscal horn attachments during gait. The extrusion distance increased as laxity increased and the amount of contact force transferred through the menisci during gait decreased rapidly as the meniscal attachments became more lax. Horn attachment lengths that were 20% longer than MRI attachment lengths resulted in an almost complete loss of force transfer through the menisci during the gait cycle. Relatively small changes (2-3mm) in the lengths at which horn bundles first become taut, manifested in large changes in the capacity of the tissue to transmit forces. As meniscal horn attachment laxity increased from 80% to 120% of the MRI measured horn distance, medial meniscus extrusion increased 3.9mm for the first subject and 2.7mm for the second subject. For the same horn laxity changes, the percent of medial tibiofemoral contact force transmitted through the medial meniscus during early stance decreased from 51% to 8% and from 36% to 14% for the two subjects. The results of our study show that increased meniscal extrusion occurs with increased laxity of the meniscal tibia attachments and this increased laxity results in loss of meniscal function.

A Subject-Specific Musculoskeletal Modeling Framework to Predict In Vivo Mechanics of Total Knee Arthroplasty

Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth “grand challenge competition to predict in vivo knee loads” dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R2) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > −0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > −0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.