Subject-Specific Finite Element Modeling of the Tibiofemoral Joint in Vivo: Development, Verification and Application

A new methodology for subject-specific finite element (FE) modeling of the tibiofemoral (TF) joint based on in vivo computed tomography (CT), magnetic resonance imaging (MRI), and dynamic stereo-radiography (DSX) data is presented. Two techniques to incorporate in vivo skeletal kinematics as FE boundary conditions were implemented and compared: one used MRI-measured tibiofemoral kinematics in a non-weight-bearing supine position and allowed five degrees of freedom at the joint in response to an axially applied force; the other used DSX-measured tibiofemoral kinematics in a weight-bearing standing position and permitted only axial translation in response to the same force. The model-predicted cartilage-cartilage contact areas were examined against ‘benchmarks’ from a novel in situ contact area analysis (ISCAA) in which the intersection volume between non-deformed femoral and tibial cartilage was characterized to determine the contact. The results showed that the DSX-based model predicted contact areas in close alignment with the benchmarks, and outperformed the MRI-based model. The importance of accurate, task-specific skeletal kinematics in subject-specific FE modeling and the necessity of subject-specific verification are discussed. A study of the effects of partial meniscectomy on the intra-articular contact mechanics was then conducted as an illustration of application of the verified models. A musculoskeletal dynamic model was used to generate the knee joint forces as boundary conditions for the above developed FE models. Thus, a sequence of quasi-static position-dependent FE models was developed for a series of time points throughout a decline walking task. These time points include heel-strike and in increments of 0.05 seconds up to 0.30 seconds, and additionally, the time points of the two peak compressive joint force values for each knee. Several factors were observed to measure the effects on intra-articular contact mechanics. The greatest maximum compressive stress was recorded in the partially meniscectomized compartment or in the opposite compartment of the contralateral knee throughout all time points. The significance of the application of the FE models for evaluation of the biomechanical effects of meniscectomy is demonstrated, and the importance of simultaneously observing joint kinematics and intra-articular contact mechanics at more than one time point during a dynamic task is discussed

Development of a finite element model of the knee using patient specific magnetic resonance imaging data and biomechanical testing of soft tissues

This thesis presents the findings of investigations carried out relating to the creation of full joint contact patient specific finite element models for correlation with biological studies in the study of Osteoarthritis (OA) development. To understand the relationship between altered loading and biological changes in articular cartilage (AC), a method for predicting stresses and strains experienced inside the tissues is required. An in-vitro study was conducted to explore the possibility of correlating finite element (FE) and gene expression study results. FE models were used to predict the stresses and strains inside the AC for explants subjected to different loading conditions. The study demonstrated that the accurate representation of AC surface geometry is crucial and current flat surface axisymmetric cylinder representations used in AC explant modelling introduces significant error in the prediction of tissue mechanical behaviour. Cutting of the AC explant to achieve a flat surface can affect the biological, mechanical and tribology behaviour of the tissue. Thus, a method for creating explant specific finite element models with the use of digital image correlation (DIC) was developed and is presented, allowing for surface layer preservation in AC explants for correlated gene expression and inverse FE. Reconstruction of tissue geometries from magnetic resonance (MR) imaging scan data of the knee was explored. It was possible to segment both hard and soft tissues from the same set of MR imaging scan data. Meshing of the geometries using a fundamentally voxel based algorithm proved to cause significant error in the segmented volume. An alternative contour based algorithm needs to be explored. Uncertainties concerning the presence and modelling of meniscotibial ligaments (MTLs) in full joint contact FE models found in literature were addressed. An anatomy study revealed that the MTLs are found in both the medial and lateral side of the joint around the periphery of the anterior, middle and posterior portion of the menisci. With the use of cross polarised light microscopy, it was established Page | VII that MTLs consist of Type I collagen orientated in the circumferential direction around the menisci. As a result, the MTLs were modelled as an anisotropic membrane. Using the full joint contact finite element model, the influence of MTLs on knee joint kinematics was investigated. It was found that the MTLs reinforce the function of the meniscal horns and circumferential fibres in the meniscus and help constrain the meniscus. Therefore, it was concluded that the MTLs are mechanically significant in the stabilisation of knee joints and should be included in knee models for accurate prediction of knee joint behaviour

Subchondral Bone Cysts - Filling the Void

Subchondral bone cysts (SBCs) are voids that can occur in the bones of young horses, especially horses intended for performance. Believed to be caused by trauma or osteochondrosis, these defects most often occur in the medial femoral condyle (MFC). Current treatments for equine SBCs have poor outcomes and have not improved over the last several decades. The gold standard for surgical treatment consists of cyst debridement and grafting. However, radiographic healing is not often reported, and when it is, only 20% of horses exhibit full radiographic healing. A novel treatment strategy has been recently introduced that places a lag screw across the SBC and has demonstrated high rates of radiographic healing. However, the mechanics of how a transcondylar lag screw could enhance SBC healing are unknown. The goals of this study were to determine a plausible mechanism of SBC initiation and growth, as well as understand the mechanics of the transcondylar lag screw. A finite element modeling approach has been taken to examine the mechanics associated with SBCs. Using CT scans from young Thoroughbred horses, several finite element models have been developed for this study. The results of this study show that high-impact loading from gallop can cause stresses high enough to initiate bone damage in a healthy equine stifle joint. Additionally, once a small defect has manifested, stresses rise even higher and further damage is likely. Medial meniscus stress also increases with a MFC SBC, which suggests that secondary injury to the medial meniscus may be due to a disrupted load path through the MFC. Furthermore, it was determined that the transcondylar screw is able to heal SBCs by providing enough mechanical stimulus to the adjacent bone to promote bone formation. Not only is the stimulus for growth present, but the screw also aligns third principal stresses transverse to trabecular orientation across the cyst. This encourages bone to form across the void, as opposed to trabecular thickening, which results in the sclerosis typically seen in MFC SBCs. Lastly, it was determined that larger cysts respond best to the transcondylar screw. Full penetration of the screw into the cystic cavity provides the highest bone-forming stimulus, and also best aligns stresses across the void. This work demonstrates that trauma can initiate SBCs and that the transcondylar screw provides a unique mechanism to enhance healing. Since humans are susceptible to a wide range of bone defects that exhibit similar characteristic of an equine SBC, it is believed that there is huge potential for translational applications

A musculoskeletal model of a subject specific knee joint with menisci, during the stance phase of a walk cycle

Title from PDF of title page, viewed on January 26, 2012Dissertation advisor: Trent M. GuessVitaIncludes bibliographic references (p. 57-62)Thesis (Ph.D.)--School of Computing and Engineering and Dept. of Mathematics and Statistics. University of Missouri--Kansas City, 2011Movement simulation and musculoskeletal modeling can predict muscle forces, but current methods are hindered by simplified representations of joint structures. Simulations that incorporate muscle forces, an anatomical representation of the natural knee, and contact mechanics would be a powerful tool in orthopedics. This study developed a subject specific computational model of the knee with menisci within the multibody framework. The model was validated with experimental measurements from a mechanical knee simulator and then it was incorporated into a neuromusculoskeletal model of a lower limb. The detailed model of a subject specific knee was developed in MD.ADAMS (MSC Software Corporation, Santa Ana, CA). This model includes femur, tibia, patella as well as lateral and medial meniscus geometries and knee ligaments of a subject specific cadaver knee (female: 78 years old, 59 kg right knee). A deformable contact with constant coefficients was applied to define the contact force between patella, femur, and tibia articular cartilages. Meniscus geometries were divided into 61 discrete elements (29 medial and 32 lateral) that were connected through 6×6 stiffness matrices. An optimization and design of experiments approach was used to determine parameters for the 6×6 stiffness matrices such that the force-displacement relationship of the meniscus matched that of a linearly elastic transversely isotropic finite element model for the same cadaver meniscus. Similarly, parameters for compliant contact models of tibio-menisco-femoral articulations were derived from finite element solutions. As a validation step, the multibody knee model was placed within a dynamic knee simulator model and the tibio-femoral and patello-femoral kinematics were compared to an identically loaded cadaver knee. Consequently, the validated knee model was incorporated into a scaled lower right limb musculoskeletal model in LifeMODTM (Lifemodeler, Inc.). A forward-dynamics muscle driven simulation of the stance phase of a gait cycle was simulated to estimate muscles and ground reaction forces. The predicted forces were evaluated using test data provided by Vaughan CL. et al. (1999).Introduction -- Materials and methods -- Results -- Discussio

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

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

The effects of posterior cruciate ligament deficiency on posterolateral corner structures under gait- and squat-loading conditions: A computational knee model

OBJECTIVES: The aim of the current study was to analyse the effects of posterior cruciate ligament (PCL) deficiency on forces of the posterolateral corner structure and on tibiofemoral (TF) and patellofemoral (PF) contact force under dynamic-loading conditions. METHODS: A subject-specific knee model was validated using a passive flexion experiment, electromyography data, muscle activation, and previous experimental studies. The simulation was performed on the musculoskeletal models with and without PCL deficiency using a novel force-dependent kinematics method under gait- and squat-loading conditions, followed by probabilistic analysis for material uncertain to be considered. RESULTS: Comparison of predicted passive flexion, posterior drawer kinematics and muscle activation with experimental measurements showed good agreement. Forces of the posterolateral corner structure, and TF and PF contact forces increased with PCL deficiency under gait- and squat-loading conditions. The rate of increase in PF contact force was the greatest during the squat-loading condition. The TF contact forces increased on both medial and lateral compartments during gait-loading conditions. However, during the squat-loading condition, the medial TF contact force tended to increase, while the lateral TF contact forces decreased. The posterolateral corner structure, which showed the greatest increase in force with deficiency of PCL under both gait- and squat-loading conditions, was the popliteus tendon (PT). CONCLUSION: PCL deficiency is a factor affecting the variability of force on the PT in dynamic-loading conditions, and it could lead to degeneration of the PF joint.Cite this article: K-T. Kang, Y-G. Koh, M. Jung, J-H. Nam, J. Son, Y.H. Lee, S-J. Kim, S-H. Kim. The effects of posterior cruciate ligament deficiency on posterolateral corner structures under gait- and squat-loading conditions: A computational knee model. Bone Joint Res 2017;6:31-42. DOI: 10.1302/2046-3758.61.BJR-2016-0184.R1.ope

Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee

Osteoarthritis of the knee is increasingly prevalent as our population ages, representing an increasing financial burden, and severely impacting quality of life. The invasiveness of in vivo procedures and the high cost of cadaveric studies has left computational tools uniquely suited to study knee biomechanics. Developments in deep learning have great potential for efficiently generating large-scale datasets to enable researchers to perform population-sized investigations, but the time and effort associated with producing robust hexahedral meshes has been a limiting factor in expanding finite element studies to encompass a population. Here we developed a fully automated pipeline capable of taking magnetic resonance knee images and producing a working finite element simulation. We trained an encoder-decoder convolutional neural network to perform semantic image segmentation on the Imorphics dataset provided through the Osteoarthritis Initiative. The Imorphics dataset contained 176 image sequences with varying levels of cartilage degradation. Starting from an open-source swept-extrusion meshing algorithm, we further developed this algorithm until it could produce high quality meshes for every sequence and we applied a template-mapping procedure to automatically place soft-tissue attachment points. The meshing algorithm produced simulation-ready meshes for all 176 sequences, regardless of the use of provided (manually reconstructed) or predicted (automatically generated) segmentation labels. The average time to mesh all bones and cartilage tissues was less than 2 min per knee on an AMD Ryzen 5600X processor, using a parallel pool of three workers for bone meshing, followed by a pool of four workers meshing the four cartilage tissues. Of the 176 sequences with provided segmentation labels, 86% of the resulting meshes completed a simulated flexion-extension activity. We used a reserved testing dataset of 28 sequences unseen during network training to produce simulations derived from predicted labels. We compared tibiofemoral contact mechanics between manual and automated reconstructions for the 24 pairs of successful finite element simulations from this set, resulting in mean root-mean-squared differences under 20% of their respective min-max norms. In combination with further advancements in deep learning, this framework represents a feasible pipeline to produce population sized finite element studies of the natural knee from subject-specific models

An Improved Polynomial Chaos Expansion Based Response Surface Method And Its Applications On Frame And Spring Engineering Based Structures

In engineering fields, computational models provide a tool that can simulate a real world response and enhance our understanding of physical phenomenas. However, such models are often computationally expensive with multiple sources of uncertainty related to the model’s input/assumptions. For example, the literature indicates that ligament’s material properties and its insertion site locations have a significant effect on the performance of knee joint models, which makes addressing uncertainty related to them a crucial step to make the computational model more representative of reality. However, previous sensitivity studies were limited due to the computational expense of the models. The high computational expense of sensitivity analysis can be addressed by performing the analysis with a reduced number of model runs or by creating an inexpensive surrogate model. Both approaches are addressed in this work by the use of Polynomial chaos expansion (PCE)-based surrogate models and design of experiments (DoE). Therefore, the objectives of this dissertation were: 1- provide guidelines for the use of PCE-based models and investigate their efficiency in case of non-linear problems. 2- utilize PCE and DoE-based tools to introduce efficient sensitivity analysis approaches to the field of knee mechanics. To achieve these objectives, a frame structure was used for the first aim, and a rigid body computational model for two knee specimens was used for the second aim. Our results showed that, for PCE-based surrogate models, once the recommended number of samples is used, increasing the PCE order produced more accurate surrogate models. This conclusion was reflected in the R2 values realized for three highly non-linear functions ( 0.9998, 0.9996 and 0.9125, respectively). Our results also showed that the use of PCE and DoE-based sensitivity analyses resulted in practically identical results with significant savings in the computational cost of sensitivity analysis when compared to a traditional quasi-Monte Carlo (MC) approach (95% and 98% reductions in model evaluations for analyses with 10 and 6 uncertain variables, respectively). Finally, the use of D-optimal DoE resulted in a reduction in the number of samples required to perform sensitivity analysis by 64.4%, which reduced the computational burden by 1018 hours