2,313 research outputs found

    Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

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    Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj

    Subject Specific Computational Models of the Knee to Predict Anterior Cruciate Ligament Injury

    Get PDF
    Knee joint is a complex joint involving multiple interactions between cartilage, bone, muscles, ligaments, tendons and neural control. Anterior Cruciate Ligament (ACL) is one ligament in the knee joint that frequently gets injured during various sports or recreational activities. ACL injuries are common in college level and professional athletes especially in females and the injury rate is growing in epidemic proportions despite significant increase in the research focusing on neuromuscular and proprioceptive training programs. Most ACL injuries lead to surgical reconstruction followed by a lengthy rehabilitation program impacting the health and performance of the athlete. Furthermore, the athlete is still at the risk of early onset of osteoarthritis. Regardless of the gender disparity in the ACL injury rates, a clear understanding of the underlying injury mechanisms is required in order to reduce the incidence of these injuries. Computational modeling is a resourceful and cost effective tool to investigate the biomechanics of the knee. The aim of this study was twofold. The first aim was to develop subject specific computational models of the knee joint and the second aim to gain an improved understanding of the ACL injury mechanisms using the subject specific models. We used a quasi-static, multi-body modeling approach and developed MRI based tibio-femoral computational knee joint models. Experimental joint laxity and combined loading data was obtained using five cadaveric knee specimens and a state-of-the-art robotic system. Ligament zero strain lengths and insertion points were optimized using joint laxity data. Combined loading and ACL strain data were used for model validations. ACL injury simulations were performed using factorial design approach comprising of multiple factors and levels to replicate a large and rich set of loading states. This thesis is an extensive work covering all the details of the ACL injury project explained above and highlighting the importance of 1) computational modeling in inj

    Specimen-Specific Natural, Pathological, and Implanted Knee Mechanics Using Finite Element Modeling

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    There is an increasing incidence of knee pain and injury among the population, and increasing demand for higher knee function in total knee replacement designs. As a result, clinicians and implant manufacturers are interested in improving patient outcomes, and evaluation of knee mechanics is essential for better diagnosis and repair of knee pathologies. Common knee pathologies include osteoarthritis (degradation of the articulating surfaces), patellofemoral pain, and cruciate ligament injury and/or rupture. The complex behavior of knee motion presents unique challenges in the diagnosis of knee pathology and restoration of healthy knee function. Quantifying knee mechanics is essential for developing successful rehabilitation therapies and surgical treatments. Researchers have used in-vitro and in-vivo experiments to quantify joint kinematics and loading, but experiments can be costly and time-intensive, and contact and ligament mechanics can be difficult to measure directly. Computational modeling can complement experimental studies by providing cost-effective solutions for quantifying joint and soft tissue forces. Musculoskeletal models have been used to measure whole-body motion, and predict joint and muscle forces, but these models can lack detail and accuracy at the joint-level. Finite element modeling provides accurate solutions of the internal stress/strain behavior of bone and soft tissue using subject-specific geometry and complex contact and material representations. While previous FE modeling has been used to simulate injury and repair, models are commonly based on literature description or average knee behavior. The research presented in this dissertation focused on developing subject-specific representations of the TF and PF joints including calibration and validation to experimental data for healthy, pathological, and implanted knee conditions. A combination of in-vitro experiment and modeling was used to compare healthy and cruciate-deficient joint mechanics, and develop subject-specific computational representations. Insight from in-vitro testing supported in-vivo simulations of healthy and implanted subjects, in which PF mechanics were compared between two common patellar component designs and the impact of cruciate ligament variability on joint kinematics and loads was assessed. The suite of computational models developed in this dissertation can be used to investigate knee pathologies to better inform clinicians on the mechanisms surrounding injury, support the diagnosis of at-risk patients, explore rehabilitation and surgical techniques for repair, and support decision-making for new innovative implant designs

    Development of a Human Tibiofemoral Joint Finite Element Model to Investigate the Effects of Obesity and Knee Malalignment on Joint Contact Pressure

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    Obesity is a known risk factor for osteoarthritis (OA). Excess body weight generates greater joint contact forces at the knee; however, obese individuals alter their gait to decrease joint contact forces. Knee malalignment has been identified as a strong mediating factor between obesity and knee OA progression. Excess body weight acting on a varus malaligned knee would have an additive effect on cartilage stress and could cause stress levels to exceed the threshold limit for damage and loss of cartilage matrix. A finite element (FE) model of the human tibiofemoral joint was developed and validated in order to investigate changes in cartilage pressure due to obesity and knee varus malalignment. The results of this analysis show that obese loading conditions caused greater contact pressure in both the lateral and medial tibiofemoral compartments at most phases of stance. Increased contact pressure applied cyclically during daily activities could make obese individuals more susceptible to OA. Varus malalignment increased medial contact pressure as expected, but lateral contact pressure also increased during midstance for both normal weight and obese load conditions. These results suggest that varus malaligned individuals could be susceptible to OA development in both tibiofemoral compartments due to the overall increase in joint contact pressure. As a qualitative tool, the FE model functioned well in highlighting changes in joint contact pressure due to the addition of obesity or varus malalignment. Further work can be done to increase confidence in the quantitative outputs of the model by using more sophisticated material models for soft tissue structures and incorporating the patellofemoral joint into the FE model

    A dynamic multibody model of the physiological knee to predict internal loads during movement in gravitational field

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    Obtaining tibio-femoral (TF) contact forces, ligament deformations and loads during daily life motor tasks would be useful to better understand the aetiopathogenesis of knee joint diseases or the effects of ligament reconstruction and knee arthroplasty. However, methods to obtain this information are either too simplified or too computationally demanding to be used for clinical application. A multibody dynamic model of the lower limb reproducing knee joint contact surfaces and ligaments was developed on the basis of magnetic resonance imaging. Several clinically relevant conditions were simulated, including resistance to hyperextension, varus\u2013valgus stability, anterior\u2013posterior drawer, loaded squat movement. Quadriceps force, ligament deformations and loads, and TF contact forces were computed. During anterior drawer test the anterior cruciate ligament (ACL) was maximally loaded when the knee was extended (392\ua0N) while the posterior cruciate ligament (PCL) was much more stressed during posterior drawer when the knee was flexed (319\ua0N). The simulated loaded squat revealed that the anterior fibres of ACL become inactive after 60\ub0 of flexion in conjunction with PCL anterior bundle activation, while most components of the collateral ligaments exhibit limited length changes. Maximum quadriceps and TF forces achieved 3.2 and 4.2 body weight, respectively. The possibility to easily manage model parameters and the low computational cost of each simulation represent key points of the present project. The obtained results are consistent with in vivo measurements, suggesting that the model can be used to simulate complex and clinically relevant exercises

    Advancement of a Forward Solution Mathematical Model of the Human Knee Joint

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    Sometimes called degenerative joint disease, osteoarthritis most often affects the knee, which is a leading cause of pain and reduced mobility. While early treatment is ideal, it is not always successful in combating osteoarthritis and improving joint function, therefore creating the need for total knee arthroplasty (TKA), which is a late-stage treatment where damaged bone and cartilage are replaced by artificial cartilage. Joint arthroplasty is a common and successful procedure for end-stage osteoarthritis. Unfortunately, TKA patient satisfaction rates lag behind those of total hip arthroplasty [1,2], which remains an impetus to create new designs. Due to ethical issues, time requirements, and prohibitive expenses of testing new designs in vivo, mathematical modeling may be an alternative tool to efficiently assess the kinetics and kinematics of new TKA designs. In general, the knee is one of the most complicated joints in the human body, including multiple articulating surfaces and the complexity of soft tissues encompassing the knee joint. Therefore, mathematically modeling the knee is a challenging and complex process. With increasing computational power and advanced knowledge and techniques, advanced mathematical models of the knee joint can be created utilizing various modeling techniques [3]. Furthermore, mathematical modeling can advance our knowledge related to knee biomechanics, especially those parameters that are otherwise challenging to obtain, such as soft tissue properties and effects pertaining to knee mechanics. Mathematical modeling allows the user to evaluate multiple designs and surgical approaches quickly and cost-efficiently without having to conduct lengthy clinical studies. Mathematical models can also provide insight into topics of clinical significance and can efficiently analyze outcome contributions that cannot be controlled in fluoroscopic studies, such as anatomical, mechanical, and kinematic alignment comparisons for the same subject. Furthermore, mathematical models can evaluate the effect of TKA design concerns such as changing conformity of the polyethylene or using femoral components with single or multi radius designs [3]. The objectives of this dissertation are to advance a forward solution model to create a more sophisticated and physiological representation of the knee joint.This is achieved by developing a muscle wrapping algorithm, integrating a validated inverse dynamics model, adding more muscles, incorporating several different TKA types including revision TKA designs, and expanding the model to include other daily activities. All these modifications are incorporated in a graphical user interface. These advancements increase both functionality and accuracy of the model. Several validation methods have been implemented to investigate the accuracy of the predicted kinetics and kinematics of this mathematical model

    Musculoskeletal Models in a Clinical Perspective

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    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

    Development of a Rigid Body Forward Solution Physiological Model of the Lower Leg to Predict Non Implanted and Implanted Knee Kinematics and Kinetics

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    This dissertation describes the development and results of a physiological rigid body forward solution mathematical model that can be used to predict normal knee and total knee arthroplasty (TKA) kinematics and kinetics. The simulated activities include active extension and weight-bearing deep knee bend. The model includes both the patellofemoral and tibiofemoral joints. Geometry of the normal or implanted knee is represented by multivariate polynomials and modeled by constraining the velocity of lateral and medial tibiofemoral and patellofemoral contact points in a direction normal to the geometry surface. Center of mass, ligament and muscle attachment points and normal knee geometry were found using computer aided design (CAD) models built from computer tomography (CT) scans of a single subject. Quadriceps forces were the input for this model and were adjusted using a unique controller to control the rate of flexion, embedded with a controller which stabilizes the patellofemoral joint. The model was developed first using normal knee parameters. Once the normal knee model was validated, different total knee arthroplasty (TKA) designs were virtually implanted. The model was validated using in vivo data obtained through fluoroscopic analysis. In vivo data of the extension and deep knee bend activities from five non-implanted knees were used to validate the normal model kinematics. In vivo kinematic and kinetic data from a telemetric TKA with a tibia component instrumented with strain gauges was used to validate the kinematic and kinetic results of the model implanted with the TKA geometry. The tibiofemoral contact movement matched the trend seen in the in vivo data from the one patient available with this implant. The maximum axial tibiofemoral force calculated with the model was in 3.1% error with the maximum force seen in the in vivo data, and the trend of the contact forces matched well. Several other TKA designs were virtually implanted and analyzed to determine kinematics and bearing surface kinetics. The comparison between the model results and those previously assessed under in vivo conditions validates the effectiveness of the model and proves that it can be used to predict the in vivo kinematic and kinetic behavior of a TKA

    Personalized musculoskeletal modeling:Bone morphing, knee joint modeling, and applications

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