ReadySim: A computational framework for building explicit finite element musculoskeletal simulations directly from motion laboratory data

Model Properties and Clinical Application in the Finite Element Analysis of Knee Joint: A Review

The knee is the most complex joint in the human body, including bony structures like the femur, tibia, fibula, and patella, and soft tissues like menisci, ligaments, muscles, and tendons. Complex anatomical structures of the knee joint make it difficult to conduct precise biomechanical research and explore the mechanism of movement and injury. The finite element model (FEM), as an important engineering analysis technique, has been widely used in many fields of bioengineering research. The FEM has advantages in the biomechanical analysis of objects with complex structures. Researchers can use this technology to construct a human knee joint model and perform biomechanical analysis on it. At the same time, finite element analysis can effectively evaluate variables such as stress, strain, displacement, and rotation, helping to predict injury mechanisms and optimize surgical techniques, which make up for the shortcomings of traditional biomechanics experimental research. However, few papers introduce what material properties should be selected for each anatomic structure of knee FEM to meet different research purposes. Based on previous finite element studies of the knee joint, this paper summarizes various modeling strategies and applications, serving as a reference for constructing knee joint models and research design.

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.

A Musculoskeletal Model for Estimating Hip Contact Pressure During Walking

Cartilage contact pressures are major factors in osteoarthritis etiology and are commonly estimated using finite element analysis (FEA). FEA models often include subject-specific joint geometry, but lack subject-specific joint kinematics and muscle forces. Musculoskeletal models use subject-specific kinematics and muscle forces but often lack methods for estimating cartilage contact pressures. Our objective was to adapt an elastic foundation (EF) contact model within OpenSim software to predict hip cartilage contact pressures and compare results to validated FEA models. EF and FEA models were built for five subjects. In the EF models, kinematics and muscle forces were applied and pressure was calculated as a function of cartilage overlap depth. Cartilage material properties were perturbed to find the best match to pressures from FEA. EF models with elastic modulus = 15 MPa and Poisson's ratio = 0.475 yielded results most comparable to FEA, with peak pressure differences of 4.34 ± 1.98 MPa (% difference = 39.96 ± 24.64) and contact area differences of 3.73 ± 2.92% (% difference = 13.4 ± 11.3). Peak pressure location matched between FEA and EF for 3 of 5 subjects, thus we do not recommend this model if the location of peak contact pressure is critically important to the research question. Contact area magnitudes and patterns matched reasonably between FEA and EF, suggesting that this model may be useful for questions related to those variables, especially if researchers desire inclusion of subject-specific geometry, kinematics, muscle forces, and dynamic motion in a computationally efficient framework.

Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling

Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.

Multiscale finite element musculoskeletal model for intact knee dynamics

BACKGROUND AND OBJECTIVE

The dynamic characteristics of the intact knee joint are valuable for treating knee osteoarthritis and designing knee prostheses. However, it remains a challenge to elucidate the detailed dynamics of the knee due to its complexity of anatomical structure and complex interaction with body dynamics.

METHODS

In this study, a unique subject-specific musculoskeletal model with a concurrent high-accuracy intact finite element knee model was created and used to simultaneously evaluate the kinematics and mechanics of an intact knee joint during the gait cycle.

RESULTS

A medial pivot motion with external rotation, and a large parallel anterior translation were observed in the stance and swing phases, respectively, which is consistent with the in vivo fluoroscopy measurements. The maximum axial contact force on the knee joint, observed at 45% of the gait cycle, is approximately 2.89 times the body weight. The medial cartilage bears 65.7% of the total axial contact force. The results demonstrate that the cartilage-cartilage contact bears most of the joint load (62.5%) compared to the cartilage-meniscus-cartilage contact (37.5%). Regarding contact mechanics, the maximum contact pressure on both sides of the tibial cartilage (8.2 MPa) is almost similar to the first axial loading peak (14%) of the gait cycle. Additionally, the maximum contact pressure (6.01 MPa) was observed during the stance phase of the gait cycle on the patellofemoral joint.

CONCLUSIONS

The predicted results on the tibiofemoral and patellofemoral joints provide a theoretical basis for the treatment of knee joint diseases and knee prosthesis design. Moreover, this approach presents a comprehensive tool to evaluate the mechanics at both the body and tissue levels. Therefore, it has a high potential for application in human biomechanics.

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.