Contribution of geometric design parameters to knee implant performance: Conflicting impact of conformity on kinematics and contact mechanics

Background: Articular geometry of knee implant has a competing impact on kinematics and contact mechanics of total knee arthroplasty (TKA) such that geometry with lower contact pressure will impose more constraints on knee kinematics. The geometric parameters that may cause this competing effect have not been well understood. This study aimed to quantify the underlying relationships between implant geometry as input and its performance metrics as output. Methods: Parametric dimensions of a fixed-bearing cruciate retaining implant were randomized to generate a number of perturbed implant geometries. Performance metrics (i.e., maximum contact pressure, anterior–posterior range of motion [A-P ROM] and internal–external range of motion [I-E ROM]) of each randomized design were calculated using finite element analysis. The relative contributions of individual geometric variables to the performance metrics were then determined in terms of sensitivity indices (SI). Results: The femoral and tibial distal or posterior radii and femoral frontal radius are the key parameters. In the sagittal plane, distal curvature of the femoral and tibial influenced both contact pressure, i.e., SI = 0.57; SI = 0.65, and A-P ROM, i.e., SI = 0.58; SI = 0.6, respectively. However, posterior curvature of the femoral and tibial implants had a smaller impact on the contact pressure, i.e., SI = 0.31; SI = 0.23 and a higher impact on the I-E ROM, i.e., SI = 0.72; SI = 0.58. It is noteworthy that in the frontal plane, frontal radius of the femoral implant impacted both contact pressure (SI = 0.38) and I-E ROM (SI = 0.35). Conclusion: Findings of this study highlighted how changes in the conformity of the femoral and tibial can impact the performance metrics

Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models

Background Finite element (FE) modeling and multibody dynamics have traditionally been applied separately to the domains of tissue mechanics and musculoskeletal movements, respectively. Simultaneous simulation of both domains is needed when interactions between tissue and movement are of interest, but this has remained largely impractical due to high computational cost. Method of Approach Here we present a method for concurrent simulation of tissue and movement, in which state of the art methods are used in each domain, and communication occurs via a surrogate modeling system based on locally weighted regression. The surrogate model only performs FE simulations when regression from previous results is not within a user-specified tolerance. For proof of concept and to illustrate feasibility, the methods were demonstrated on an optimization of jumping movement using a planar musculoskeletal model coupled to a FE model of the foot. To test the relative accuracy of the surrogate model outputs against those of the FE model, a single forward dynamics simulation was performed with FE calls at every integration step and compared with a corresponding simulation with the surrogate model included. Neural excitations obtained from the jump height optimization were used for this purpose and root mean square (RMS) difference between surrogate and FE model outputs (ankle force and moment, peak contact pressure and peak von Mises stress) were calculated. Results Optimization of jump height required 1800 iterations of the movement simulation, each requiring thousands of time steps. The surrogate modeling system only used the FE model in 5% of time steps, i.e. a 95% reduction of computation time. Errors introduced by the surrogate model were less than 1 mm in jump height and RMS errors of less than 2 N in ground reaction force, 0.25 Nm in ankle moment, and 10 kPa in peak tissue stress. Conclusion Adaptive surrogate modeling based on local regression allows efficient concurrent simulations of tissue mechanics and musculoskeletal movement

Adaptive Surrogate Modeling for Efficient Coupling of Musculoskeletal Control and Tissue Deformation Models

Background Finite element (FE) modeling and multibody dynamics have traditionally been applied separately to the domains of tissue mechanics and musculoskeletal movements, respectively. Simultaneous simulation of both domains is needed when interactions between tissue and movement are of interest, but this has remained largely impractical due to high computational cost. Method of Approach Here we present a method for concurrent simulation of tissue and movement, in which state of the art methods are used in each domain, and communication occurs via a surrogate modeling system based on locally weighted regression. The surrogate model only performs FE simulations when regression from previous results is not within a user-specified tolerance. For proof of concept and to illustrate feasibility, the methods were demonstrated on an optimization of jumping movement using a planar musculoskeletal model coupled to a FE model of the foot. To test the relative accuracy of the surrogate model outputs against those of the FE model, a single forward dynamics simulation was performed with FE calls at every integration step and compared with a corresponding simulation with the surrogate model included. Neural excitations obtained from the jump height optimization were used for this purpose and root mean square (RMS) difference between surrogate and FE model outputs (ankle force and moment, peak contact pressure and peak von Mises stress) were calculated. Results Optimization of jump height required 1800 iterations of the movement simulation, each requiring thousands of time steps. The surrogate modeling system only used the FE model in 5% of time steps, i.e. a 95% reduction of computation time. Errors introduced by the surrogate model were less than 1 mm in jump height and RMS errors of less than 2 N in ground reaction force, 0.25 Nm in ankle moment, and 10 kPa in peak tissue stress. Conclusion Adaptive surrogate modeling based on local regression allows efficient concurrent simulations of tissue mechanics and musculoskeletal movement

A comparative study of surrogate musculoskeletal models using various neural network configurations

Title from PDF of title page, viewed on August 13, 2013Thesis advisor: Reza R. DerakhshaniVitaIncludes bibliographic references (pages 85-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The central idea in musculoskeletal modeling is to be able to predict body-level (e.g. muscle forces) as well as tissue-level information (tissue-level stress, strain, etc.). To develop computationally efficient techniques to analyze such models, surrogate models have been introduced which concurrently predict both body-level and tissue-level information using multi-body and finite-element analysis, respectively. However, this kind of surrogate model is not an optimum solution as it involves the usage of finite element models which are computation intensive and involve complex meshing methods especially during real-time movement simulations. An alternative surrogate modeling method is the use of artificial neural networks in place of finite-element models. The ultimate objective of this research is to predict tissue-level stresses experienced by the cartilage and ligaments during movement and achieve concurrent simulation of muscle force and tissue stress using various surrogate neural network models, where stresses obtained from finite-element models provide the frame of reference. Over the last decade, neural networks have been successfully implemented in several biomechanical modeling applications. Their adaptive ability to learn from examples, simple implementation techniques, and fast simulation times make neural networks versatile and robust when compared to other techniques. The neural network models are trained with reaction forces from multi-body models and stresses from finite element models obtained at the interested elements. Several configurations of static and dynamic neural networks are modeled, and accuracies close to 93% were achieved, where the correlation coefficient is the chosen measure of goodness. Using neural networks, the simulation time was reduced nearly 40,000 times when compared to the finite-element models. This study also confirms theoretical concepts that special network configurations--including average committee, stacked generalization, and negative correlation learning--provide considerably better results when compared to individual networks themselves.Introduction -- Methods -- Results -- Conclusion -- Future work -- Appendix A. Various linear and non-linear modeling techniques -- Appendix B. Error analysi

Chondrocyte Deformations as a Function of Tibiofemoral Joint Loading Predicted by a Generalized High-Throughput Pipeline of Multi-Scale Simulations

Cells of the musculoskeletal system are known to respond to mechanical loading and chondrocytes within the cartilage are not an exception. However, understanding how joint level loads relate to cell level deformations, e.g. in the cartilage, is not a straightforward task. In this study, a multi-scale analysis pipeline was implemented to post-process the results of a macro-scale finite element (FE) tibiofemoral joint model to provide joint mechanics based displacement boundary conditions to micro-scale cellular FE models of the cartilage, for the purpose of characterizing chondrocyte deformations in relation to tibiofemoral joint loading. It was possible to identify the load distribution within the knee among its tissue structures and ultimately within the cartilage among its extracellular matrix, pericellular environment and resident chondrocytes. Various cellular deformation metrics (aspect ratio change, volumetric strain, cellular effective strain and maximum shear strain) were calculated. To illustrate further utility of this multi-scale modeling pipeline, two micro-scale cartilage constructs were considered: an idealized single cell at the centroid of a 100×100×100 μm block commonly used in past research studies, and an anatomically based (11 cell model of the same volume) representation of the middle zone of tibiofemoral cartilage. In both cases, chondrocytes experienced amplified deformations compared to those at the macro-scale, predicted by simulating one body weight compressive loading on the tibiofemoral joint. In the 11 cell case, all cells experienced less deformation than the single cell case, and also exhibited a larger variance in deformation compared to other cells residing in the same block. The coupling method proved to be highly scalable due to micro-scale model independence that allowed for exploitation of distributed memory computing architecture. The method’s generalized nature also allows for substitution of any macro-scale and/or micro-scale model providing application for other multi-scale continuum mechanics problems

Musculoskeletal Models in a Clinical Perspective

This book includes a selection of papers showing the potential of the dynamic modelling approach to treat problems related to the musculoskeletal system. The state-of-the-art is presented in a review article and in a perspective paper, and several examples of application in different clinical problems are provided

Biomécanique de l'articulation du genou humain durant la marche - un modèle musculosquelettique hybride

RÉSUMÉ L’articulation du genou est l’une des articulations les plus complexes du corps humain. Elle est exposée à des charges et des mouvements de grandeurs importantes pendant les activités professionnelles, récréatives et même quotidiennes. Cet environnement mécanique exigeant l’expose à diverses contraintes et déformations excessives, des blessures impliquant à la fois les articulations patello-fémorales (PF) et tibio-fémorales (TF). L'arthrose (OA) est l'un des troubles musculo-squelettiques les plus répandus touchant environ 27 millions d'adultes aux États-Unis seulement. La rupture du ligament croisé antérieur (LCA) est également une lésion articulaire commune avec une prévalence beaucoup plus élevée chez les sujets féminins que chez les sujets masculins. Une bonne connaissance de la biomécanique fonctionnelle de l’articulation du genou et des facteurs qui l'affectent, dans des conditions saines et pathologiques, est une condition préalable pour élaborer des stratégies efficaces pour la prévention et le traitement de ces blessures. Les modèles musculo-squelettiques (MS) de l'extrémité inférieure promettent d'améliorer notre compréhension de la fonction articulaire du genou, de ses blessures et aussi des programmes de prévention et des traitements associés. Plusieurs modèles analytiques et d'éléments finis (EF) avec différents degrés de précision et de raffinement ont été développés. Ils se sont présentés comme une alternative fiable aux méthodes expérimentales qui ont des limitations majeures, principalement liées à leurs coûts élevés, aux difficultés liées aux précisions des mesures et à la reproduction parfois impossible de certaines situations physiologiques. Cependant, de nombreuses hypothèses sont souvent formulées dans certains modèles MS (lors de l'estimation des forces musculaires et des forces de contacts articulaires). Le genou est généralement idéalisé comme une articulation 2D avec son mouvement contraint dans le plan sagittal, négligeant ainsi les déplacements et les équations d'équilibre dans les plans restants. Avec les forces musculaires estimées, l'équilibre statique dans le plan frontal est donc considéré pour estimer les forces du plateau tibial négligeant la résistance passive du genou, la géométrie articulaire, et en supposant des centres de contact médial/latéral fixes. Pour évaluer les effets de telles hypothèses, un modèle MS hybride de l'extrémité inférieure incluant un modèle élément finis (EF) du genou 3D a été utilisé pour simuler la phase d’appui de la marche.----------ABSTRACT Human knee joints experience loads and movements of substantial magnitudes during occupational, recreational and even regular daily living activities. This demanding mechanical environment exposes them to a host of painful and debilitating deformities, injuries and degenerations involving both patellofemoral (PF) and tibiofemoral (TF) articulations. Osteoarthritis (OA) is one of the most prevalent musculoskeletal (MS) disorders affecting approximately 27 million adults in the US alone. ACL rupture is, also, a common joint injury with much higher prevalence reported in female athletes compared to their male counterparts. Effective preventive measures and treatment managements of such disorders require a sound knowledge of the joint behavior in both healthy and pathologic conditions. MS modeling of the lower extremity is promising to improve the current understanding of the knee joint function and injuries and consequently associated prevention and treatment programs. Several analytical and finite element (FE) models with different degrees of precision and refinement have been developed. They are considered as a reliable alternative to experimental methods that have major limitations, mainly related to their high costs, difficulties related to measurement accuracy and reproduction of some physiological situations. However, numerous assumptions are often made in some MS models (when estimating muscle forces and joint contact loads). The knee is commonly idealized as a planar (2D) joint with its motion constrained to remain in the sagittal plane, neglecting thus both displacements and equilibrium equations in remaining planes. With muscle forces predicted, the static equilibrium in the frontal plane is consequently considered to estimate tibial compartmental loads neglecting the knee joint passive resistance, the knee geometry, and assuming medial/lateral contact centers. To evaluate the effects of such assumptions, a hybrid MS model of the lower extremity incorporating a detailed validated 3D knee FE model was used to simulate the stance phase of gait. This model of the knee joint is made of bony structures (tibia, femur and patella) and their compliant cartilage layers as well as menisci, major TF (anterior cruciate ligament, ACL; posterior cruciate ligament, PCL; lateral collateral ligament, LCL; medial collateral ligament, MCL) and PF (medial PF ligament, MPFL; lateral PF ligament, LPFL) ligaments, patellar tendon (PT), and lower extremity muscles (e.g., quadriceps, hamstrings and gastrocnemius)