Computational Comparison of Medializing Tibial Tubercle Osteotomy and Trochleoplasty in Patients with Trochlear Dysplasia

Medial patellofemoral ligament reconstruction (MPFLR) has emerged as the procedure of choice for recurrent patellar dislocation. This addresses soft tissue injury but does not address underlying anatomic factors, including trochlear dysplasia, that are commonly present and increase risk of dislocation. Quantification of the stability offered by other surgical interventions, namely, medializing tibial tubercle osteotomy (mTTO) and trochleoplasty, with and without MPFLR, may provide insight for surgical choices in patients with trochlear dysplasia. We developed subject-specific finite element models based on magnetic resonance scans from a cohort of 20 patients with trochlear dysplasia and recurrent patellar dislocation. The objectives of this study were (1) to compare patella stability after mTTO and trochleoplasty procedures; (2) to evaluate whether it is necessary to perform an MPFLR in combination with the mTTO or trocheoplasty procedure; and (3) to quantify the robustness of patellar stability to variability in knee kinematics. Trochleoplasty performed better than mTTO at stabilizing the patella between 5° and 30° flexion. For both mTTO and trochleoplasty procedures, it was beneficial to also perform MPFLR—inclusion of MPFLR halved the magnitude of patellar laxity predicted in the simulations. Simulations that did not include any medial patellofemoral ligament restraint were also more sensitive to variation in tibiofemoral internal–external kinematics

Patellar mechanics during simulated kneeling in the natural and implanted knee

AbstractKneeling is required during daily living for many patients after total knee replacement (TKR), yet many patients have reported that they cannot kneel due to pain, or avoid kneeling due to discomfort, which critically impacts quality of life and perceived success of the TKR procedure. The objective of this study was to evaluate the effect of component design on patellofemoral (PF) mechanics during a kneeling activity. A computational model to predict natural and implanted PF kinematics and bone strains after kneeling was developed and kinematics were validated with experimental cadaveric studies. PF joint kinematics and patellar bone strains were compared for implants with dome, medialized dome, and anatomic components. Due to the less conforming nature of the designs, change in sagittal plane tilt as a result of kneeling at 90° knee flexion was approximately twice as large for the medialized-dome and dome implants as the natural case or anatomic implant, which may result in additional stretching of the quadriceps. All implanted cases resulted in substantial increases in bone strains compared with the natural knee, but increased strains in different regions. The anatomic patella demonstrated increased strains inferiorly, while the dome and medialized dome showed increases centrally. An understanding of the effect of implant design on patellar mechanics during kneeling may ultimately provide guidance to component designs that reduces the likelihood of knee pain and patellar fracture during kneeling

Computational Lower Limb Simulator Boundary Conditions to Reproduce Measured TKA Loading in a Cohort of Telemetric Implant Patients

Recent advancements in computational modeling offer opportunities to refine total knee arthroplasty (TKA) design and treatment strategies. This study developed patient-specific simulator external boundary conditions (EBCs) using a PID-controlled lower limb finite element (FE) model. Calibration of the external actuation required to achieve measured patient-specific joint loading and motion was completed for nine patients with telemetric implants during gait, stair descent, and deep knee bend. The study also compared two EBC scenarios: activity-specific hip AP motion and pelvic rotation (that was averaged across all patients for an activity) and patient-specific hip AP motion and pelvic rotation. Including patient-specific data significantly improved reproduction of joint-level loading, reducing root mean squared error between the target and achieved loading by 28.7% and highlighting the importance of detailed patient data in replicating joint kinematics and kinetics. The principal component analysis (PCA) of the EBCs for the patient dataset showed that one component represented 77.8% of the overall variation, while the first three components represented 97.8%. Given the significant loading variability within the patient cohort, this group of patient-specific models can be run individually to provide insight into expected TKA mechanics variability, and the PCA can be utilized to further create reasonable EBCs that expand the variability evaluated

A Computationally Efficient Strategy to Estimate Muscle Forces in a Finite Element Musculoskeletal Model of the Lower Limb

Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation

A Computationally Efficient Strategy to Estimate Muscle Forces in a Finite Element Musculoskeletal Model of the Lower Limb

Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation

Automated Finite Element Meshing of the Lumbar Spine: Verification and Validation with 18 Specimen-specific Models

The purpose of this study was to seek broad verification and validation of human lumbar spine finite element models created using a previously published automated algorithm. The automated algorithm takes segmented CT scans of lumbar vertebrae, automatically identifies important landmarks and contact surfaces, and creates a finite element model. Mesh convergence was evaluated by examining changes in key output variables in response to mesh density. Semi-direct validation was performed by comparing experimental results for a single specimen to the automated finite element model results for that specimen with calibrated material properties from a prior study. Indirect validation was based on a comparison of results from automated finite element models of 18 individual specimens, all using one set of generalized material properties, to a range of data from the literature. A total of 216 simulations were run and compared to 186 experimental data ranges in all six primary bending modes up to 7.8 Nm with follower loads up to 1000 N. Mesh convergence results showed less than a 5% difference in key variables when the original mesh density was doubled. The semi-direct validation results showed that the automated method produced results comparable to manual finite element modeling methods. The indirect validation results showed a wide range of outcomes due to variations in the geometry alone. The studies showed that the automated models can be used to reliably evaluate lumbar spine biomechanics, specifically within our intended context of use: in pure bending modes, under relatively low non-injurious simulated in vivo loads, to predict torque rotation response, disc pressures, and facet forces

Statistical Shape Modeling predicts Patellar Bone Geometry to Enable Stereo-radiographic Kinematic Tracking

Complications in the patellofemoral (PF) joint of patients with total knee replacements include patellar subluxation and dislocation, and remain a cause for revision. Kinematic measurements to assess these complications and evaluate implant designs require the accuracy of dynamic stereo-radiographic systems with 3D-2D registration techniques. While tibiofemoral kinematics are typically derived by tracking metallic implants, PF kinematic measurements are difficult as the patellar implant is radiotransparent and a representation of the resected patella bone requires either pre-surgical imaging and precise implant placement or post-surgical imaging. Statistical shape models (SSMs), used to characterize anatomic variation, provide an alternative means to obtain the representation of the resected patella for use in kinematic tracking. Using a virtual platform of a stereo-radiographic system, the objectives of this study were to evaluate the ability of an SSM to predict subject-specific 3D implanted patellar geometries from simulated 2D image profiles, and to formulate an effective data collection methodology for PF kinematics by considering accuracy for a variety of patient pose scenarios. An SSM of the patella was developed for 50 subjects and a leave-one-out approach compared SSM-predicted and actual geometries; average 3D errors were 0.45 ± 0.07 mm (mean ± standard deviation), which is comparable to the accuracy of traditional segmentation. Further, initial imaging of the patella in five unique stereo radiographic perspectives yielded the most accurate representation. The ability to predict the remaining patellar geometry of the implanted PF joint with radiographic images and SSM, instead of CT, can reduce radiation exposure and streamline in vivo kinematic evaluations

Computational Approach to Correcting Joint Instability in Patients with Recurrent Patellar Dislocation

Patellar dislocation is a debilitating injury common in active adolescents and young adults. Conservative treatment after initial dislocation is often recommended, but almost half of these patients continue to suffer from recurrent dislocation. The objective of this study was to compare preoperative patellofemoral joint stability with stability after a series of simulated procedures, including restorative surgery to correct to pre-injury state, generic tibial tubercle osteotomy, patient-specific reconstructive surgery to correct anatomic abnormality, less invasive patient-specific surgery, and equivalent healthy controls. Three-dimensional, subject-specific finite element models of the patellofemoral joint were developed for 28 patients with recurrent patellar dislocation. A 50 N lateral load was applied to the patella to assess the lateral stability of the patellofemoral joint at 10° intervals from 0° to 40° flexion. Medial patellofemoral ligament reconstruction, along with reconstructive procedures to correct anatomic abnormality were simulated. Of all the simulations performed, the healthy equivalent control models showed the least patellar internal–external rotation, medial–lateral translation, and medial patellofemoral ligament restraining load during lateral loading tests. Isolated restorative medial patellofemoral ligament reconstruction was the surgery that resulted in the most patellar internal–external rotation, medial–lateral translation, and medial patellofemoral ligament reaction force across all flexion angles. Patient-specific reconstruction to correct anatomic abnormality was the only surgical group to have non-significantly different results compared with the healthy equivalent control group across all joint stability metrics evaluated. Statement of clinical significance: This study suggests patient-specific reconstructive surgery that corrects underlying anatomic abnormalities best reproduces the joint stability of an equivalent healthy control when compared with the pre-injury state, generic tibial tubercle osteotomy, and less invasive patient-specific surgery

A Lower Extremity Model for Muscle-Driven Simulation of Activity Using Explicit Finite Element Modeling

A key strength of computational modeling is that it can provide estimates of muscle, ligament, and joint loads, stresses, and strains through non-invasive means. However, simulations that can predict the forces in the muscles during activity while maintaining sufficient complexity to realistically represent the muscles and joint structures can be computationally challenging. For this reason, the current state of the art is to apply separate rigid-body dynamic and finite-element (FE) analyses in series. However, the use of two or more disconnected models often fails to capture key interactions between the joint-level and whole-body scales. Single framework MSFE models have the potential to overcome the limitations associated with disconnected models in series. The objectives of the current study were to create a multi-scale FE model of the human lower extremity that combines optimization, dynamic muscle modeling, and structural FE analysis in a single framework and to apply this framework to evaluate the mechanics of healthy knee specimens during two activities. Two subject-specific FE models (Model 1, Model 2) of the lower extremity were developed in ABAQUS/Explicit including detailed representations of the muscles. Muscle forces, knee joint loading, and articular contact were calculated for two activities using an inverse dynamics approach and static optimization. Quadriceps muscle forces peaked at the onset of chair rise (2174 N, 1962 N) and in early stance phase (510 N, 525 N), while gait saw peak forces in the hamstrings (851 N, 868 N) in midstance. Joint forces were similar in magnitude to available telemetric patient data. This study demonstrates the feasibility of detailed quasi-static, muscle-driven simulations in an FE framework

Automated Finite Element Meshing of the Lumbar Spine: Verification and Validation with 18 Specimen-specific Models

The purpose of this study was to seek broad verification and validation of human lumbar spine finite element models created using a previously published automated algorithm. The automated algorithm takes segmented CT scans of lumbar vertebrae, automatically identifies important landmarks and contact surfaces, and creates a finite element model. Mesh convergence was evaluated by examining changes in key output variables in response to mesh density. Semi-direct validation was performed by comparing experimental results for a single specimen to the automated finite element model results for that specimen with calibrated material properties from a prior study. Indirect validation was based on a comparison of results from automated finite element models of 18 individual specimens, all using one set of generalized material properties, to a range of data from the literature. A total of 216 simulations were run and compared to 186 experimental data ranges in all six primary bending modes up to 7.8 Nm with follower loads up to 1000 N. Mesh convergence results showed less than a 5% difference in key variables when the original mesh density was doubled. The semi-direct validation results showed that the automated method produced results comparable to manual finite element modeling methods. The indirect validation results showed a wide range of outcomes due to variations in the geometry alone. The studies showed that the automated models can be used to reliably evaluate lumbar spine biomechanics, specifically within our intended context of use: in pure bending modes, under relatively low non-injurious simulated in vivo loads, to predict torque rotation response, disc pressures, and facet forces