A Combined Experimental-Computational Method to Generate Reliable Subject Specific Models of the Knee's Ligamentous Constraint

With the advancement of computational models of the knee, the opportunity exists to utilize patient-specific computational models of the knee intra-operatively to assist surgeons. A critical component for evaluation of whole knee mechanics is configuration of the soft tissue ligament structures surrounding the knee. The overarching purpose of the current research was to develop a unique methodology, utilizing both experimental and computational techniques, for efficient development of patient-specific ligament constraint model. To this end, an experimental method to manually assess knee laxity was developed, and used to evaluate changes in knee laxity after total knee replacement in eight cadaveric specimens. A computational model of ligament constraint was developed to complement the knee laxity data collected during the experimental protocol. A sensitivity study performed on the model identified the most critical ligament parameters affecting knee laxity. Subsequently, these ligament parameters were optimized using the simulated annealing algorithm to minimize the difference between the model predicted knee laxity and the experimentally observed knee laxity for four cadaveric specimens. The optimized ligament parameters were used to predict knee kinematics during an experimental assessment in a quasi-static knee loading rig. Knee kinematic predictions using the optimized ligament parameters were compared to predictions using previously published ligament parameters, and subsequently reduced the RMS difference between the predictions and the experimental kinematics by more than 50% for knee rotations

Instantaneous Generation of Subject-Specific Finite Element Models of the Hip Capsule

Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software

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

Development of a Statistical Shape-Function Model of the Implanted Knee for Real-Time Prediction of Joint Mechanics

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implanted knee to instantaneously predict joint mechanics in an efficient manner. Finite element methods were combined with Latin hypercube sampling and regression analyses to produce modeling equations relating nine implant design and six surgical alignment parameters to tibiofemoral (TF) joint mechanics outcomes during a deep knee bend. A SFM was developed and TF contact mechanics, kinematics, and soft tissue loads were instantaneously predicted from the model. Average normalized root-mean-square error predictions were between 2.79% and 9.42%, depending on the number of parameters included in the model. The statistical shape-function model generated instantaneous joint mechanics predictions using a maximum of 130 training simulations, making it ideally suited for integration into a patient-specific design and alignment optimization pipeline. Such a tool may be used to optimize kinematic function to achieve more natural motion or minimize implant wear, and may aid the engineering and clinical communities in improving patient satisfaction and surgical outcomes

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven models require comprehensive and representative training datasets to be generalizable across the movement variability seen in the population at large. Bottlenecks in model development frequently occur due to the lack of sufficient training data and the significant time and resources necessary to acquire these datasets. Reliable methods to generate synthetic biomechanical training data could streamline model development and potentially improve model performance. In this study, we developed a methodology to generate synthetic kinematics and the associated predicted IMU signals using open source musculoskeletal modeling software. These synthetic data were used to train neural networks to predict three degree-of-freedom joint rotations at the hip and knee during gait either in lieu of or along with previously measured experimental gait data. The accuracy of the models’ kinematic predictions was assessed using experimentally measured IMU signals and gait kinematics. Models trained using the synthetic data out-performed models using only the experimental data in five of the six rotational degrees of freedom at the hip and knee. On average, root mean square errors in joint angle predictions were improved by 38% at the hip (synthetic data RMSE: 2.3°, measured data RMSE: 4.5°) and 11% at the knee (synthetic data RMSE: 2.9°, measured data RMSE: 3.3°), when models trained solely on synthetic data were compared to measured data. When models were trained on both measured and synthetic data, root mean square errors were reduced by 54% at the hip (measured + synthetic data RMSE: 1.9°) and 45% at the knee (measured + synthetic data RMSE: 1.7°), compared to measured data alone. These findings enable future model development for different activities of clinical significance without the burden of generating large quantities of gait lab data for model training, streamlining model development, and ultimately improving model performance

Development of a Statistical Shape-Function Model of the Implanted Knee for Real-Time Prediction of Joint Mechanics

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implanted knee to instantaneously predict joint mechanics in an efficient manner. Finite element methods were combined with Latin hypercube sampling and regression analyses to produce modeling equations relating nine implant design and six surgical alignment parameters to tibiofemoral (TF) joint mechanics outcomes during a deep knee bend. A SFM was developed and TF contact mechanics, kinematics, and soft tissue loads were instantaneously predicted from the model. Average normalized root-mean-square error predictions were between 2.79% and 9.42%, depending on the number of parameters included in the model. The statistical shape-function model generated instantaneous joint mechanics predictions using a maximum of 130 training simulations, making it ideally suited for integration into a patient-specific design and alignment optimization pipeline. Such a tool may be used to optimize kinematic function to achieve more natural motion or minimize implant wear, and may aid the engineering and clinical communities in improving patient satisfaction and surgical outcomes

High Resolution Three-dimensional Strain Mapping of Bioprosthetic Heart Valves using Digital Image Correlation

Transcatheter aortic valve replacement (TAVR) is a safe and effective treatment option for patients deemed at high and intermediate risk for surgical aortic valve replacement. Similar to surgical aortic valves (SAVs), transcatheter aortic valves (TAVs) undergo calcification and mechanical wear over time. However, to date, there have been limited publications on the long-term durability of TAV devices. To assess longevity and mechanical strength of TAVs in comparison to surgical bioprosthetic valves, three-dimensional deformation analysis and strain measurement of the leaflets become an inevitable part of the evaluation. The goal of this study was to measure and compare leaflet displacement and strain of two commonly used TAVs in a side-by-side comparison with a commonly used SAV using a high-resolution digital image correlation (DIC) system. 26-mm Edwards SAPIEN 3, 26-mm Medtronic CoreValve, and 25-mm Carpentier-Edwards PERIMOUNT Magna surgical bioprosthesis were examined in a custom-made valve testing apparatus. A time-varying, spatially uniform pressure was applied to the leaflets at different loading rates. GOM ARAMIS® software was used to map leaflet displacement and strain fields during loading and unloading. High displacement regions were found to be at the leaflet belly region of the three bioprosthetic valves. In addition, the frame of the surgical bioprosthesis was found to be remarkably flexible, in contrary to CoreValve and SAPIEN 3 in which the stent was nearly rigid under a similar loading condition. The experimental DIC measurements can be used to characterize the anisotropic materiel behavior of the bioprosthetic heart valve leaflets and validate heart valve computational simulations

High Resolution Three-dimensional Strain Mapping of Bioprosthetic Heart Valves using Digital Image Correlation

Transcatheter aortic valve replacement (TAVR) is a safe and effective treatment option for patients deemed at high and intermediate risk for surgical aortic valve replacement. Similar to surgical aortic valves (SAVs), transcatheter aortic valves (TAVs) undergo calcification and mechanical wear over time. However, to date, there have been limited publications on the long-term durability of TAV devices. To assess longevity and mechanical strength of TAVs in comparison to surgical bioprosthetic valves, three-dimensional deformation analysis and strain measurement of the leaflets become an inevitable part of the evaluation. The goal of this study was to measure and compare leaflet displacement and strain of two commonly used TAVs in a side-by-side comparison with a commonly used SAV using a high-resolution digital image correlation (DIC) system. 26-mm Edwards SAPIEN 3, 26-mm Medtronic CoreValve, and 25-mm Carpentier-Edwards PERIMOUNT Magna surgical bioprosthesis were examined in a custom-made valve testing apparatus. A time-varying, spatially uniform pressure was applied to the leaflets at different loading rates. GOM ARAMIS® software was used to map leaflet displacement and strain fields during loading and unloading. High displacement regions were found to be at the leaflet belly region of the three bioprosthetic valves. In addition, the frame of the surgical bioprosthesis was found to be remarkably flexible, in contrary to CoreValve and SAPIEN 3 in which the stent was nearly rigid under a similar loading condition. The experimental DIC measurements can be used to characterize the anisotropic materiel behavior of the bioprosthetic heart valve leaflets and validate heart valve computational simulations

Computational Evaluation of TKR Stability Using Feedback-Controlled Compressive Loading

Pre-clinical assessment of stability in total knee replacement is crucial for developing preferred implant performance. Current total knee replacement patients often experience joint instability that the human body addresses with compensatory strategies. Specifically, an increased quadriceps-hamstrings co-contraction serves to increase joint stability through an increased compressive force across the tibiofemoral joint. The aim of this study is to introduce a novel method to evaluate total knee replacement by determining the compressive loading required to achieve natural knee stability. Four current total knee replacement geometries in both their cruciate-retaining and posterior-stabilized forms are modeled in a finite-element framework. The finite-element model is initially validated experimentally using traditional knee laxity testing with a constant compressive load and anterior-posterior displacement or internal-external rotation. Model predictions of constraint are in reasonable agreement with experimental results (average root mean square errors: 0.46 Nm, 62.5 N). The finite-element model is subsequently interfaced with a feedback controller to vary the compressive force that the implant requires in order to match experimental natural knee internal-external and anteriorposterior stability at different flexion angles. Results show that the lower constraint total knee replacement designs require on average 66.7% more compressive load than the higher constraint designs to achieve natural knee constraint. As expected, total knee replacement stability and compressive load requirements to replicate natural kinematics vary with inclusion of tibiofemoral ligaments. The current study represents a novel approach to evaluate stability in existing total knee replacement geometries and to design implants that better restore natural knee mechanics

Validation of Model-Predicted Tibial Tray-Synthetic Bone Relative Motion in Cementless Total Knee Replacement During Activities of Daily Living

As fixation of cementless total knee replacement components during the first 4-6 weeks after surgery is crucial to establish bony ingrowth into the porous surface, several studies have quantified implant-bone micromotion. Relative motion between the tray and bone can be measured in vitro, but the full micromotion contour map cannot typically be accessed experimentally. Finite element models have been employed to estimate the full micromotion map, but have not been directly validated over a range of loading conditions. The goal of this study was to develop and validate computational models for the prediction of tray-bone micromotion under simulated activities of daily living. Gait, stair descent and deep knee bend were experimentally evaluated on four samples of a cementless tibial tray implanted into proximal tibial Sawbones™ constructs. Measurements of the relative motion between the tray and the anterior cortical shell were collected with digital image correlation and used to validate a finite element model that replicated the experiment. Additionally, a probabilistic analysis was performed to account for experimental uncertainty and determine model sensitivity to alignment and frictional parameters. The finite element models were able to distinguish between activities and capture the experimental trends. Best-matching simulations from the probabilistic analysis matched measured displacement with an average root mean square (RMS) difference of 14.3 µm and Pearson-product correlation of 0.93, while the mean model presented an average RMS difference of 27.1 µm and a correlation of 0.8. Maximum deviations from average experimental measurements were 40.5 and 87.1 µm for the best-matching and average simulations, respectively. The computational pipeline developed in this study can facilitate and enhance pre-clinical assessment of novel implant components