FEBio: History and Advances

Modeling Fatigue Failure of Cartilage and Fibrous Biological Tissues Using Constrained Reactive Mixture Theory

Fatigue failure in biological soft tissues plays a critical role in the etiology of chronic soft tissue injuries and diseases such as osteoarthritis (OA). Understanding failure mechanisms is hindered by the decades-long timescales over which damage takes place. Analyzing the factors contributing to fatigue failure requires the help of validated computational models developed for soft tissues. This study presents a framework for fatigue failure of fibrous biological tissues based on reaction kinetics, where the composition of intact and fatigued material regions can evolve via degradation and breakage over time, in response to energy-based fatigue and damage criteria. Using reactive constrained mixture theory, material region mass fractions are governed by the axiom of mass balance. Progression of fatigue is controlled by an energy-based reaction rate, with user-selected probability functions defining the damage propensity of intact and fatigued material regions. Verification of this reactive theory, which is implemented in the open-source FEBio finite element software, is provided in this study. Validation is also demonstrated against experimental data, showing that predicted damage can be linked to results from biochemical assays. The framework is also applied to study fatigue failure during frictional contact of cartilage. Simulating previous experiments suggests that frictional effects slightly increase fatigue progression, but the main driver is cyclic compressive contact loading. This study demonstrated the ability of theoretical models to complement and extend experimental findings, advancing our understanding of the time progression of fatigue in biological tissues.

Integration of Febio as an Instructional Tool in the Undergraduate Biomechanics Curriculum

Computer simulations play an important role in a range of biomedical engineering applications. Thus, it is important that biomedical engineering students engage with modeling in their undergraduate education and establish an understanding of its practice. In addition, computational tools enhance active learning and complement standard pedagogical approaches to promote student understanding of course content. Herein, we describe the development and implementation of learning modules for computational modeling and simulation (CM&S) within an undergraduate biomechanics course. We developed four CM&S learning modules that targeted predefined course goals and learning outcomes within the febio studio software. For each module, students were guided through CM&S tutorials and tasked to construct and analyze more advanced models to assess learning and competency and evaluate module effectiveness. Results showed that students demonstrated an increased interest in CM&S through module progression and that modules promoted the understanding of course content. In addition, students exhibited increased understanding and competency in finite element model development and simulation software use. Lastly, it was evident that students recognized the importance of coupling theory, experiments, and modeling and understood the importance of CM&S in biomedical engineering and its broad application. Our findings suggest that integrating well-designed CM&S modules into undergraduate biomedical engineering education holds much promise in supporting student learning experiences and introducing students to modern engineering tools relevant to professional development.

Linel2D-Net: A deep learning approach to solving 2D linear elastic boundary value problems on image domains

Efficient solution of physical boundary value problems (BVPs) remains a challenging task demanded in many applications. Conventional numerical methods require time-consuming domain discretization and solving techniques that have limited throughput capabilities. Here, we present an efficient data-driven DNN approach to non-iterative solving arbitrary 2D linear elastic BVPs. Our results show that a U-Net-based surrogate model trained on a representative set of reference FDM solutions can accurately emulate linear elastic material behavior with manifold applications in deformable modeling and simulation.

Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Reproducibility in modeling and simulation of the knee: Academic, industry, and regulatory perspectives

Stakeholders in the modeling and simulation (M&S) community organized a workshop at the 2019 Annual Meeting of the Orthopaedic Research Society (ORS) entitled "Reproducibility in Modeling and Simulation of the Knee: Academic, Industry, and Regulatory Perspectives." The goal was to discuss efforts among these stakeholders to address irreproducibility in M&S focusing on the knee joint. An academic representative from a leading orthopedic hospital in the United States described a multi-institutional, open effort funded by the National Institutes of Health to assess model reproducibility in computational knee biomechanics. A regulatory representative from the United States Food and Drug Administration indicated the necessity of standards for reproducibility to increase utility of M&S in the regulatory setting. An industry representative from a major orthopedic implant company emphasized improving reproducibility by addressing indeterminacy in personalized modeling through sensitivity analyses, thereby enhancing preclinical evaluation of joint replacement technology. Thought leaders in the M&S community stressed the importance of data sharing to minimize duplication of efforts. A survey comprised 103 attendees revealed strong support for the workshop and for increasing emphasis on computational modeling at future ORS meetings. Nearly all survey respondents (97%) considered reproducibility to be an important issue. Almost half of respondents (45%) tried and failed to reproduce the work of others. Two-thirds of respondents (67%) declared that individual laboratories are most responsible for ensuring reproducible research whereas 44% thought that journals are most responsible. Thought leaders and survey respondents emphasized that computational models must be reproducible and credible to advance knee M&S.

Finite Element Implementation of Computational Fluid Dynamics With Reactive Neutral and Charged Solute Transport in FEBio

The objective of this study was to implement a novel fluid-solutes solver into the open-source finite element software FEBio, that extended available modeling capabilities for biological fluids and fluid-solute mixtures. Using a reactive mixture framework, this solver accommodates diffusion, convection, chemical reactions, electrical charge effects, and external body forces, without requiring stabilization methods that were deemed necessary in previous computational implementations of the convection-diffusion-reaction equation at high Peclet numbers. Verification and validation problems demonstrated the ability of this solver to produce solutions for Peclet numbers as high as 1011, spanning the range of physiological conditions for convection-dominated solute transport. This outcome was facilitated by the use of a formulation that accommodates realistic values for solvent compressibility, and by expressing the solute mass balance such that it properly captured convective transport by the solvent and produced a natural boundary condition of zero diffusive solute flux at outflow boundaries. Since this numerical scheme was not necessarily foolproof, guidelines were included to achieve better outcomes that minimize or eliminate the potential occurrence of numerical artifacts. The fluid-solutes solver presented in this study represents an important and novel advancement in the modeling capabilities for biomechanics and biophysics as it allows modeling of mechanobiological processes via the incorporation of chemical reactions involving neutral or charged solutes within dynamic fluid flow. The incorporation of charged solutes in a reactive framework represents a significant novelty of this solver. This framework also applies to a broader range of nonbiological applications.

Computational study of biomechanical drivers of renal cystogenesis

Renal cystogenesis is the pathological hallmark of autosomal dominant polycystic kidney disease, caused by PKD1 and PKD2 mutations. The formation of renal cysts is a common manifestation in ciliopathies, a group of syndromic disorders caused by mutation of proteins involved in the assembly and function of the primary cilium. Cystogenesis is caused by the derailment of the renal tubular architecture and tissue deformation that eventually leads to the impairment of kidney function. However, the biomechanical imbalance of cytoskeletal forces that are altered in cells with Pkd1 mutations has never been investigated, and its nature and extent remain unknown. In this computational study, we explored the feasibility of various biomechanical drivers of renal cystogenesis by examining several hypothetical mechanisms that may promote morphogenetic markers of cystogenesis. Our objective was to provide physics-based guidance for our formulation of hypotheses and our design of experimental studies investigating the role of biomechanical disequilibrium in cystogenesis. We employed the finite element method to explore the role of (1) wild-type versus mutant cell elastic modulus; (2) contractile stress magnitude in mutant cells; (3) localization and orientation of contractile stress in mutant cells; and (4) sequence of cell contraction and cell proliferation. Our objective was to identify the factors that produce the characteristic tubular cystic growth. Results showed that cystogenesis occurred only when mutant cells contracted along the apical-basal axis, followed or accompanied by cell proliferation, as long as mutant cells had comparable or lower elastic modulus than wild-type cells, with their contractile stresses being significantly greater than their modulus. Results of these simulations allow us to focus future in vitro and in vivo experimental studies on these factors, helping us formulate physics-based hypotheses for renal tubule cystogenesis.

Modeling Inelastic Responses Using Constrained Reactive Mixtures

This study reviews the progression of our research, from modeling growth theories for cartilage tissue engineering, to the formulation of constrained reactive mixture theories to model inelastic responses in any solid material, such as theories for damage mechanics, viscoelasticity, plasticity, and elasto-plastic damage. In this framework, multiple solid generations α can co-exist at any given time in the mixture. The oldest generation is denoted by α = s and is called the master generation, whose reference configuration X s is observable. The solid generations α are all constrained to share the same velocity v s , but may have distinct reference configurations X α . An important element of this formulation is that the time-invariant mapping F α s = ∂ X α / ∂ X s between these reference configurations is a function of state, whose mathematical formulation is postulated by constitutive assumption. Thus, reference configurations X α are not observable ( α ≠ s ) . This formulation employs only observable state variables, such as the deformation gradient F s of the master generation and the referential mass concentrations ρ r α of each generation, in contrast to classical formulations of inelastic responses which rely on internal state variable theory, requiring evolution equations for those hidden variables. In constrained reactive mixtures, the evolution of the mass concentrations is governed by the axiom of mass balance, using constitutive models for the mass supply densities ρ ˆ r α . Classical and constrained reactive mixture approaches share considerable mathematical analogies, as they both introduce a multiplicative decomposition of the deformation gradient, also requiring evolution equations to track some of the state variables. However, they also differ at a fundamental level, since one adopts only observable state variables while the other introduces hidden state variables. In summary, this review presents an alternative foundational approach to the modeling of inelastic responses in solids, grounded in the classical framework of mixture theory.

FDM data driven U-Net as a 2D Laplace PINN solver

Efficient solution of partial differential equations (PDEs) of physical laws is of interest for manifold applications in computer science and image analysis. However, conventional domain discretization techniques for numerical solving PDEs such as Finite Difference (FDM), Finite Element (FEM) methods are unsuitable for real-time applications and are also quite laborious in adaptation to new applications, especially for non-experts in numerical mathematics and computational modeling. More recently, alternative approaches to solving PDEs using the so-called Physically Informed Neural Networks (PINNs) received increasing attention because of their straightforward application to new data and potentially more efficient performance. In this work, we present a novel data-driven approach to solve 2D Laplace PDE with arbitrary boundary conditions using deep learning models trained on a large set of reference FDM solutions. Our experimental results show that both forward and inverse 2D Laplace problems can efficiently be solved using the proposed PINN approach with nearly real-time performance and average accuracy of 94% for different types of boundary value problems compared to FDM. In summary, our deep learning based PINN PDE solver provides an efficient tool with various applications in image analysis and computational simulation of image-based physical boundary value problems.

The effects of leaflet material properties on the simulated function of regurgitant mitral valves

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated (1) the influence of tissue extensibility and (2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known.

The Effects of leaflet material properties on the simulated function of regurgitant mitral valves

Advances in three-dimensional imaging provide the ability to construct and analyze finite element (FE) models to evaluate the biomechanical behavior and function of atrioventricular valves. However, while obtaining patient-specific valve geometry is now possible, non-invasive measurement of patient-specific leaflet material properties remains nearly impossible. Both valve geometry and tissue properties play a significant role in governing valve dynamics, leading to the central question of whether clinically relevant insights can be attained from FE analysis of atrioventricular valves without precise knowledge of tissue properties. As such we investigated 1) the influence of tissue extensibility and 2) the effects of constitutive model parameters and leaflet thickness on simulated valve function and mechanics. We compared metrics of valve function (e.g., leaflet coaptation and regurgitant orifice area) and mechanics (e.g., stress and strain) across one normal and three regurgitant mitral valve (MV) models with common mechanisms of regurgitation (annular dilation, leaflet prolapse, leaflet tethering) of both moderate and severe degree. We developed a novel fully-automated approach to accurately quantify regurgitant orifice areas of complex valve geometries. We found that the relative ordering of the mechanical and functional metrics was maintained across a group of valves using material properties up to 15% softer than the representative adult mitral constitutive model. Our findings suggest that FE simulations can be used to qualitatively compare how differences and alterations in valve structure affect relative atrioventricular valve function even in populations where material properties are not precisely known.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision

OBJECTIVES

The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries.

DESIGN

Narrative review.

METHODS

Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field.

RESULTS

Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions.

CONCLUSIONS

Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.

Percutaneous delivery and degradation of a shape memory elastomer poly(glycerol dodecanedioate) in porcine pulmonary arteries

Shape memory biodegradable elastomers are an emergent class of biomaterials well-suited for percutaneous cardiovascular repair requiring nonlinear elastic materials with facile handling. We have previously developed a chemically crosslinked shape memory elastomer, poly (glycerol dodecanedioate) (PGD), exhibiting tunable transition temperatures around body temperature (34-38 °C), exhibiting nonlinear elastic properties approximating cardiac tissues, and favorable degradation rates in vitro. Degree of tissue coverage, degradation and consequent changes in polymer thermomechanical properties, and inflammatory response in preclinical animal models are unknown material attributes required for translating this material into cardiovascular devices. This study investigates changes in the polymer structure, tissue coverage, endothelialization, and inflammation of percutaneously implanted PGD patches (20 mm × 9 mm x 0.5 mm) into the branch pulmonary arteries of Yorkshire pigs for three months. After three months in vivo, 5/8 samples exhibited (100%) tissue coverage, 2/8 samples exhibited 85-95% tissue coverage, and 1/8 samples exhibited limited (<20%) tissue coverage with mild-moderate inflammation. PGD explants showed a (60-70%) volume loss and (25-30%) mass loss, and a reduction in polymer crosslinks. Lumenal and mural surfaces and the cross-section of the explant demonstrated evidence of degradation. This study validates PGD as an appropriate cardiovascular engineering material due to its propensity for rapid tissue coverage and uneventful inflammatory response in a preclinical animal model, establishing a precedent for consideration in cardiovascular repair applications.

Three Dimensional Lower Extremity Musculoskeletal Geometry of the Visible Human Female and Male

Models and simulations of human function impact medicine and medical technology. Particularly, musculoskeletal modeling provides an avenue for insight into the human body, which might not be otherwise possible. However, reaching the ultimate goal of functional multi-scale human models has been slowed by the lack of freely available datasets of anatomical models and geometries. Moreover, female-specific geometries have been neglected with a widespread emphasis on male geometry. To help realize this goal, we have developed and shared complete three-dimensional musculoskeletal geometries extracted from the National Libraries of Medicine Visible Human Female and Male cryosections. Muscle, bone, cartilage, ligament, and fat from the pelvis to the ankle were digitized and exported. These geometries provide a foundation for continued work in human musculoskeletal simulation with high-fidelity deformable tissues that enable a better understanding of normal function and the evaluation of pathologies and treatments. This work is novel as it includes both the male and female Visible Human specimens, outputs at multiple levels of post-processing for maximum data reuse, and is publicly available.

Flow-based method demonstrates improved accuracy for calculating wall shear stress in arterial flows from 4D flow MRI data

Four-dimensional flow magnetic resonance imaging (i.e., 4D flow MRI) has become a valuable tool for the in vivo assessment of blood flow within large vessels and cardiac chambers. As wall shear stress (WSS) has been correlated with the development and progression of cardiovascular disease, focus has been directed at developing techniques to quantify WSS directly from 4D flow MRI data. The goal of this study was to compare the accuracy of two such techniques - termed the velocity and flow-based methods - in the setting of simplified and complex flow scenarios. Synthetic MR data were created from exact solutions to the Navier-Stokes equations for the steady and pulsatile flow of an incompressible, Newtonian fluid through a rigid cylinder. In addition, synthetic MR data were created from the predicted velocity fields derived from a fluid-structure interaction (FSI) model of pulsatile flow through a thick-walled, multi-layered model of the carotid bifurcation. Compared to the analytical solutions for steady and pulsatile flow, the flow-based method demonstrated greater accuracy than the velocity-based method in calculating WSS across all changes in fluid velocity/flow rate, tube radius, and image signal-to-noise (p < 0.001). Furthermore, the velocity-based method was more sensitive to boundary segmentation than the flow-based method. When compared to results from the FSI model, the flow-based method demonstrated greater accuracy than the velocity-based method with average differences in time-averaged WSS of 0.31 ± 1.03 Pa and 0.45 ± 1.03 Pa, respectively (p <0.005). These results have implications on the utility, accuracy, and clinical translational of methods to determine WSS from 4D flow MRI.

Clinical grade manufacture of 3D printed patient specific biodegradable devices for pediatric airway support

Implantable patient-specific devices are the next frontier of personalized medicine, positioned to improve the quality of care across multiple clinical disciplines. Translation of patient-specific devices requires time- and cost-effective processes to design, verify and validate in adherence to FDA guidance for medical device manufacture. In this study, we present a generalized strategy for selective laser sintering (SLS) of patient-specific medical devices following the prescribed guidance for additive manufacturing of medical devices issued by the FDA in 2018. We contextualize this process for manufacturing an Airway Support Device, a life-saving tracheal and bronchial implant restoring airway patency for pediatric patients diagnosed with tracheobronchomalacia and exhibiting partial or complete airway collapse. The process covers image-based modeling, design inputs, design verification, material inputs and verification, device verification, and device validation, including clinical results. We demonstrate how design and material assessment lead to verified Airway Support Devices that achieve desired airway patency and reduction in required Positive End-Expiratory Pressure (PEEP) after patient implantation. We propose this process as a template for general quality control of patient-specific, 3D printed implants.

A Computational Framework for Atrioventricular Valve Modeling Using Open-Source Software

Atrioventricular valve regurgitation is a significant cause of morbidity and mortality in patients with acquired and congenital cardiac valve disease. Image-derived computational modeling of atrioventricular valves has advanced substantially over the last decade and holds particular promise to inform valve repair in small and heterogeneous populations, which are less likely to be optimized through empiric clinical application. While an abundance of computational biomechanics studies has investigated mitral and tricuspid valve disease in adults, few studies have investigated its application to vulnerable pediatric and congenital heart populations. Further, to date, investigators have primarily relied upon a series of commercial applications that are neither designed for image-derived modeling of cardiac valves nor freely available to facilitate transparent and reproducible valve science. To address this deficiency, we aimed to build an open-source computational framework for the image-derived biomechanical analysis of atrioventricular valves. In the present work, we integrated an open-source valve modeling platform, SlicerHeart, and an open-source biomechanics finite element modeling software, FEBio, to facilitate image-derived atrioventricular valve model creation and finite element analysis. We present a detailed verification and sensitivity analysis to demonstrate the fidelity of this modeling in application to three-dimensional echocardiography-derived pediatric mitral and tricuspid valve models. Our analyses achieved an excellent agreement with those reported in the literature. As such, this evolving computational framework offers a promising initial foundation for future development and investigation of valve mechanics, in particular collaborative efforts targeting the development of improved repairs for children with congenital heart disease.

Model-Directed Design of Tissue Engineering Scaffolds

Scaffold-based tissue engineering requires a resorbable scaffold that can restore function and guide regeneration. Recent advances in material fabrication have expanded our control of compositional and architectural features to approach the complexity of native tissue. However, iterative scaffold design to balance multiple design targets toward optimizing regenerative performance remains both challenging and time-consuming. The number of design parameter combinations for scaffold manufacturing to achieve target properties is nearly limitless. Although a trial-and-error experimental approach may lead to a favorable scaffold design, the time and costs associated with such an approach are enormous. Computational optimization approaches are well suited to sample such a multidimensional design space and streamline the identification of target scaffold parameters for experimental evaluation. In this computational biomechanics approach, target fabrication parameters are identified by using a computational model to iterate across design parameter combinations (input) and predict the resulting graft properties (output). Herein, we describe the three key stages of this model-directed scaffold design: (1) model development, verification, and validation; (2) in silico optimization; and (3) model-directed scaffold fabrication and testing. Although there are several notable examples that have demonstrated the potential of this approach, additional accurate and physiologically appropriate computational simulations are needed to expand its utility across the field. In addition, continued advances in material fabrication are needed to provide the requisite control and resolution to produce the model-directed design. Finally, and most importantly, active collaboration between experts in materials science and computational modeling are critical to realize the full potential of this approach to accelerate scaffold development.

FEBio finite element model of a pediatric cervical spine

OBJECTIVE

The underlying biomechanical differences between the pediatric and adult cervical spine are incompletely understood. Computational spine modeling can address that knowledge gap. Using a computational method known as finite element modeling, the authors describe the creation and evaluation of a complete pediatric cervical spine model.

METHODS

Using a thin-slice CT scan of the cervical spine from a 5-year-old boy, a 3D model was created for finite element analysis. The material properties and boundary and loading conditions were created and model analysis performed using open-source software. Because the precise material properties of the pediatric cervical spine are not known, a published parametric approach of scaling adult properties by 50%, 25%, and 10% was used. Each scaled finite element model (FEM) underwent two types of simulations for pediatric cadaver testing (axial tension and cardinal ranges of motion [ROMs]) to assess axial stiffness, ROM, and facet joint force (FJF). The authors evaluated the axial stiffness and flexion-extension ROM predicted by the model using previously published experimental measurements obtained from pediatric cadaveric tissues.

RESULTS

In the axial tension simulation, the model with 50% adult ligamentous and annulus material properties predicted an axial stiffness of 49 N/mm, which corresponded with previously published data from similarly aged cadavers (46.1 ± 9.6 N/mm). In the flexion-extension simulation, the same 50% model predicted an ROM that was within the range of the similarly aged cohort of cadavers. The subaxial FJFs predicted by the model in extension, lateral bending, and axial rotation were in the range of 1-4 N and, as expected, tended to increase as the ligament and disc material properties decreased.

CONCLUSIONS

A pediatric cervical spine FEM was created that accurately predicts axial tension and flexion-extension ROM when ligamentous and annulus material properties are reduced to 50% of published adult properties. This model shows promise for use in surgical simulation procedures and as a normal comparison for disease-specific FEMs.

A Hybrid Biphasic Mixture Formulation for Modeling Dynamics in Porous Deformable Biological Tissues

The primary aim of this study is to establish the theoretical foundations for a solid-fluid biphasic mixture domain that can accommodate inertial effects and a viscous interstitial fluid, which can interface with a dynamic viscous fluid domain. Most mixture formulations consist of constituents that are either all intrinsically incompressible or compressible, thereby introducing inherent limitations. In particular, mixtures with intrinsically incompressible constituents can only model wave propagation in the porous solid matrix, whereas those with compressible constituents require internal variables, and related evolution equations, to distinguish the compressibility of the solid and fluid under hydrostatic pressure. In this study, we propose a hybrid framework for a biphasic mixture where the skeleton of the porous solid is intrinsically incompressible but the interstitial fluid is compressible. We define a state variable as a measure of the fluid volumetric strain. Within an isothermal framework, the Clausius-Duhem inequality shows that a function of state arises for the fluid pressure as a function of this strain measure. We derive jump conditions across hybrid biphasic interfaces, which are suitable for modeling hydrated biological tissues. We then illustrate this framework using confined compression and dilatational wave propagation analyses. The governing equations for this hybrid biphasic framework reduce to those of the classical biphasic theory whenever the bulk modulus of the fluid is set to infinity and inertia terms and viscous fluid effects are neglected. The availability of this novel framework facilitates the implementation of finite element solvers for fluid-structure interactions at interfaces between viscous fluids and porous-deformable biphasic domains, which can include fluid exchanges across those interfaces.

Designing a 3D Printing Based Auxetic Cardiac Patch with hiPSC-CMs for Heart Repair

Myocardial infarction is one of the largest contributors to cardiovascular disease and reduces the ability of the heart to pump blood. One promising therapeutic approach to address the diminished function is the use of cardiac patches composed of biomaterial substrates and cardiac cells. These patches can be enhanced with the application of an auxetic design, which has a negative Poisson’s ratio and can be modified to suit the mechanics of the infarct and surrounding cardiac tissue. Here, we examined multiple auxetic models (orthogonal missing rib and re-entrant honeycomb in two orientations) with tunable mechanical properties as a cardiac patch substrate. Further, we demonstrated that 3D printing based auxetic cardiac patches of varying thicknesses (0.2, 0.4, and 0.6 mm) composed of polycaprolactone and gelatin methacrylate can support induced pluripotent stem cell-derived cardiomyocyte function for 14-day culture. Taken together, this work shows the potential of cellularized auxetic cardiac patches as a suitable tissue engineering approach to treating cardiovascular disease.

3D bioprinting of a trachea-mimetic cellular construct of a clinically relevant size

Despite notable advances in extrusion-based 3D bioprinting, it remains a challenge to create a clinically-sized cellular construct using extrusion-based 3D printing due to long printing times adversely affecting cell viability and functionality. Here, we present an advanced extrusion-based 3D bioprinting strategy composed of a two-step printing process to facilitate creation of a trachea-mimetic cellular construct of clinically relevant size. A porous bellows framework is first printed using typical extrusion-based 3D printing. Selective printing of cellular components, such as cartilage rings and epithelium lining, is then performed on the outer grooves and inner surface of the bellows framework by a rotational printing process. With this strategy, 3D bioprinting of a trachea-mimetic cellular construct of clinically relevant size is achieved in significantly less total printing time compared to a typical extrusion-based 3D bioprinting strategy which requires printing of an additional sacrificial material. Tracheal cartilage formation was successfully demonstrated in a nude mouse model through a subcutaneous implantation study of trachea-mimetic cellular constructs wrapped with a sinusoidal-patterned tubular mesh preventing rapid resorption of cartilage rings in vivo. This two-step 3D bioprinting for a trachea-mimetic cellular construct of clinically relevant size can provide a fundamental step towards clinical translation of 3D bioprinting based tracheal reconstruction.

On the use of constrained reactive mixtures of solids to model finite deformation isothermal elastoplasticity and elastoplastic damage mechanics

This study presents a framework for plasticity and elastoplastic damage mechanics by treating materials as reactive solids whose internal composition evolves in response to applied loading. Using the framework of constrained reactive mixtures, plastic deformation is accounted for by allowing loaded bonds within the material to break and reform in a stressed state. Bonds which break and reform represent a new generation with a new reference configuration, which is time-invariant and provided by constitutive assumption. The constitutive relation for the reference configuration of each generation may depend on the selection of a suitable yield measure. The choice of this measure and the resulting plastic flow conditions are constrained by the Clausius-Duhem inequality. We show that this framework remains consistent with classical plasticity approaches and principles. Verification of this reactive plasticity framework, which is implemented in the open source FEBio finite element software (febio.org), is performed against standard 2D and 3D benchmark problems. Damage is incorporated into this reactive framework by allowing loaded bonds to break permanently according to a suitable damage measure, where broken bonds can no longer store free energy. Validation is also demonstrated against experimental data for problems involving plasticity and plastic damage. This study demonstrates that it is possible to formulate simple elastoplasticity and elastoplastic damage models within a consistent framework which uses measures of material mass composition as theoretically observable state variables. This theoretical frame can be expanded in scope to account for more complex behaviors.

The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries

This paper explores the use of biomechanics in identifying the mechanistic causes of musculoskeletal tissue injury and degeneration. It appraises how biomechanics has been used to develop training programmes aiming to maintain or recover tissue health. Tissue health depends on the functional mechanical environment experienced by tissues during daily and rehabilitation activities. These environments are the result of the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing musculoskeletal tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and deformation), which may be enabled by appropriate real-time biofeedback. Recent research shows that biofeedback technologies may increase their quality and effectiveness by integrating a personalised neuromusculoskeletal modelling driven by real-time motion capture and medical imaging. Model personalisation is crucial in obtaining physically and physiologically valid predictions of tissue biomechanics. Model real-time execution is crucial and achieved by code optimisation and artificial intelligence methods. Furthermore, recent work has also shown that laboratory-based motion capture biomechanical measurements and modelling can be performed outside the laboratory with wearable sensors and artificial intelligence. The next stage is to combine these technologies into well-designed easy to use products to guide training to maintain or recover tissue health in the real-world.

Finite Element Implementation of Biphasic-Fluid Structure Interactions in febio

In biomechanics, solid-fluid mixtures have commonly been used to model the response of hydrated biological tissues. In cartilage mechanics, this type of mixture, where the fluid and solid constituents are both assumed to be intrinsically incompressible, is often called a biphasic material. Various physiological processes involve the interaction of a viscous fluid with a porous-hydrated tissue, as encountered in synovial joint lubrication, cardiovascular mechanics, and respiratory mechanics. The objective of this study was to implement a finite element solver in the open-source software febio that models dynamic interactions between a viscous fluid and a biphasic domain, accommodating finite deformations of both domains as well as fluid exchanges between them. For compatibility with our recent implementation of solvers for computational fluid dynamics (CFD) and fluid-structure interactions (FSI), where the fluid is slightly compressible, this study employs a novel hybrid biphasic formulation where the porous skeleton is intrinsically incompressible but the fluid is also slightly compressible. The resulting biphasic-FSI (BFSI) implementation is verified against published analytical and numerical benchmark problems, as well as novel analytical solutions derived for the purposes of this study. An illustration of this BFSI solver is presented for two-dimensional (2D) airflow through a simulated face mask under five cycles of breathing, showing that masks significantly reduce air dispersion compared to the no-mask control analysis. In addition, we model three-dimensional (3D) blood flow in a bifurcated carotid artery assuming porous arterial walls and verify that mass is conserved across all fluid-permeable boundaries. The successful formulation and implementation of this BFSI solver offers enhanced multiphysics modeling capabilities that are accessible via an open-source software platform.

Development and analytical validation of a finite element model of fluid transport through osteochondral tissue

Fluid transport is critical to joint health. In this study we evaluate an unexplored component of joint fluid transport -fluid transport between cartilage and bone. Such transport across the cartilage-bone interface could potentially provide chondrocytes with an additional source of nutrients and signaling molecules. A biphasic viscoelastic model using an ellipsoidal fiber distribution was created with three distinct layers of cartilage (superficial zone, middle zone, and deep zone) along with a layer of subchondral bone. For stress-relaxation in unconfined compression, our results for compressive stress, radial stress, and effective fluid pressure were compared with established biphasic analytical solutions. Our model also shows the development of fluid pressure gradients at the cartilage-bone interface during loading. Fluid pressure gradients that develop at the cartilage-bone interface show consistently higher pressures in cartilage following the initial loading to 10% stain, followed by convergence of the pressures in cartilage and bone during the 400 s relaxation period. These results provide additional evidence that fluid is transported between cartilage and bone during loading and improves upon estimates of the magnitude of this effect through incorporating a realistic distribution and estimate of the collagen ultrastructure. Understanding fluid transport between cartilage and bone may be key to new insights about the mechanical and biological environment of both tissues in health and disease.

Preclinical assessment of clinically streamlined, 3D-printed, biocompatible single- and two-stage tissue scaffolds for ear reconstruction

Auricular reconstruction is a technically demanding procedure requiring significant surgical expertise, as the current gold standard involves hand carving of the costal cartilage into an auricular framework and re-implantation of the tissue. 3D-printing presents a powerful tool that can reduce technical demands associated with the procedure. Our group compared clinical, radiological, histological, and biomechanical outcomes in single- and two-stage 3D-printed auricular tissue scaffolds in an athymic rodent model. Briefly, an external anatomic envelope of a human auricle was created using DICOM computed tomography (CT) images and modified in design to create a two-stage, lock-in-key base and elevating platform. Single- and two-stage scaffolds were 3D-printed by laser sintering poly-L-caprolactone (PCL) then implanted subcutaneously in five athymic rats each. Rats were monitored for ulcer formation, site infection, and scaffold distortion weekly, and scaffolds were explanted at 8 weeks with analysis using microCT and histologic staining. Nonlinear finite element analysis was performed to determine areas of high strain in relation to ulcer formation. Scaffolds demonstrated precise anatomic appearance and maintenance of integrity of both anterior and posterior auricular surfaces and scaffold projection, with no statistically significant differences in complications noted between the single- and two-staged implantation. While minor superficial ulcers occurred most commonly at the lateral and superior helix coincident with finite element predictions of high skin strains, evidence of robust tissue ingrowth and angiogenesis was visible grossly and histologically. This promising preclinical small animal model supports future initiatives for making clinically viable options for an ear tissue scaffold.

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.

Finite element modeling to compare craniocervical motion in two age-matched pediatric patients without or with Down syndrome: implications for the role of bony geometry in craniocervical junction instability

OBJECTIVE

Instability of the craniocervical junction (CCJ) is a well-known finding in patients with Down syndrome (DS); however, the relative contributions of bony morphology versus ligamentous laxity responsible for abnormal CCJ motion are unknown. Using finite element modeling, the authors of this study attempted to quantify those relative differences.

METHODS

Two CCJ finite element models were created for age-matched pediatric patients, a patient with DS and a control without DS. Soft tissues and ligamentous structures were added based on bony landmarks from the CT scans. Ligament stiffness values were assigned using published adult ligament stiffness properties. Range of motion (ROM) testing determined that model behavior most closely matched pediatric cadaveric data when ligament stiffness values were scaled down to 25% of those found in adults. These values, along with those assigned to the other soft-tissue materials, were identical for each model to ensure that the only variable between the two was the bone morphology. The finite element models were then subjected to three types of simulations to assess ROM, anterior-posterior (AP) translation displacement, and axial tension.

RESULTS

The DS model exhibited more laxity than the normal model at all levels for all of the cardinal ROMs and AP translation. For the CCJ, the flexion-extension, lateral bending, axial rotation, and AP translation values predicted by the DS model were 40.7%, 52.1%, 26.1%, and 39.8% higher, respectively, than those for the normal model. When simulating axial tension, the soft-tissue structural stiffness values predicted by the DS and normal models were nearly identical.

CONCLUSIONS

The increased laxity exhibited by the DS model in the cardinal ROMs and AP translation, along with the nearly identical soft-tissue structural stiffness values exhibited in axial tension, calls into question the previously held notion that ligamentous laxity is the sole explanation for craniocervical instability in DS.

Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy

Recent explorations of knee biomechanics have benefited from computational modeling, specifically leveraging advancements in finite element analysis and rigid body dynamics of joint and tissue mechanics. A large number of models have emerged with different levels of fidelity in anatomical and mechanical representation. Adapted modeling and simulation processes vary widely, based on justifiable choices in relation to anticipated use of the model. However, there are situations where modelers' decisions seem to be subjective, arbitrary, and difficult to rationalize. Regardless of the basis, these decisions form the "art" of modeling, which impact the conclusions of simulation-based studies on knee function. These decisions may also hinder the reproducibility of models and simulations, impeding their broader use in areas such as clinical decision making and personalized medicine. This document summarizes an ongoing project that aims to capture the modeling and simulation workflow in its entirety-operation procedures, deviations, models, by-products of modeling, simulation results, and comparative evaluations of case studies and applications. The ultimate goal of the project is to delineate the art of a cohort of knee modeling teams through a publicly accessible, transparent approach and begin to unravel the complex array of factors that may lead to a lack of reproducibility. This manuscript outlines our approach along with progress made so far. Potential implications on reproducibility, on science, engineering, and training of modeling and simulation, on modeling standards, and on regulatory affairs are also noted.

Simulation of Skeletal Muscles in Real-Time with Parallel Computing in GPU

Modeling and simulation of the skeletal muscles are usually solved using the Finite Element method (FEM) which, although accurate, commonly needs a complex mesh and the solution is not processed in real-time. In this work, a meshfree model that simulates skeletal muscles considering their functioning and control based on electrical activity, their structure based on biological tissue, and that computes in real-time, is presented. Meshfree methods were used because they are able to surpass most of the limitations that are present in mesh-based methods. The muscular belly was modelled as a particle-based viscoelastic fluid, which is controlled using the monodomain model and shape matching. The smoothed particle hydrodynamics (SPH) method was used to solve both the fluid dynamics and the electrophysiological model. To analyze the accuracy of the method, a similar model was implemented with FEM. Both FEM and SPH methods provide similar solutions of the models in terms of pressure and displacement, with an error of around 0.09, with up to a 10% difference between them. Through the use of General-purpose computing on graphics processing units (GPGPU), real-time simulations that offer a viable alternative to mesh-based models for interactive biological tissue simulations was achieved.

Evaluation of human nasal cartilage nonlinear and rate dependent mechanical properties

Nasal reconstruction frequently requires donor cartilage and tissue, and ideally, donor tissue will closely emulate native nasal cartilage mechanics. Tissue engineering scaffolds, especially 3D printed scaffolds, have been proposed for nasal reconstruction, and the success of these constructs may depend on how well scaffolds reflect native nasal cartilage mechanical properties. Therefore, consistent and comprehensive characterization of native nasal cartilage mechanical properties is a foundation for nasal cartilage tissue engineering and reconstruction in general by providing design targets for reconstructive materials. Our group has previously shown the feasibility of producing scaffolds with porous architecture permitting chondrocyte growth and cartilage production. In this study, we determined the nonlinear and stress relaxation behavior of human nasal cartilage under unconfined compression. We then fit this experimental data to nonlinear elastic, nonlinear viscoelastic and nonlinear biphasic constitutive models. The resulting coefficients will provide design targets for nasal reconstruction and scaffold design as well as outcome measures for assessment of tissue engineered nasal cartilage.

The atlantoaxial capsular ligaments and transverse ligament are the primary stabilizers of the atlantoaxial joint in the craniocervical junction: a finite element analysis

OBJECTIVE

Prior studies have provided conflicting evidence regarding the contribution of key ligamentous structures to atlantoaxial (AA) joint stability. Many of these studies employed cadaveric techniques that are hampered by the inherent difficulties of testing isolated-injury scenarios. Analysis with validated finite element (FE) models can overcome some of these limitations. In a previous study, the authors completed an FE analysis of 5 subject-specific craniocervical junction (CCJ) models to investigate the biomechanics of the occipitoatlantal joint and identify the ligamentous structures essential for its stability. Here, the authors use these same CCJ FE models to investigate the biomechanics of the AA joint and to identify the ligamentous structures essential for its stability.

METHODS

Five validated CCJ FE models were used to simulate isolated- and combined ligamentous–injury scenarios of the transverse ligament (TL), tectorial membrane (TM), alar ligament (AL), occipitoatlantal capsular ligament, and AA capsular ligament (AACL). All models were tested with rotational moments (flexion-extension, axial rotation, and lateral bending) and anterior translational loads (C2 constrained with anterior load applied to the occiput) to simulate physiological loading and to assess changes in the atlantodental interval (ADI), a key radiographic indicator of instability.

RESULTS

Isolated AACL injury significantly increased range of motion (ROM) under rotational moment at the AA joint for flexion, lateral bending, and axial rotation, which increased by means of 28.0% ± 10.2%, 43.2% ± 15.4%, and 159.1% ± 35.1%, respectively (p ≤ 0.05 for all). TL removal simulated under translational loads resulted in a significant increase in displacement at the AA joint by 89.3% ± 36.6% (p < 0.001), increasing the ADI from 2.7 mm to 4.5 mm. An AACL injury combined with an injury to any other ligament resulted in significant increases in ROM at the AA joint, except when combined with injuries to both the TM and the ALs. Similarly, injury to the TL combined with injury to any other CCJ ligament resulted in a significant increase in displacement at the AA joint (significantly increasing ADI) under translational loads.

CONCLUSIONS

Using FE modeling techniques, the authors showed a significant reliance of isolated- and combined ligamentous–injury scenarios on the AACLs and TL to restrain motion at the AA joint. Isolated injuries to other structures alone, including the AL and TM, did not result in significant increases in either AA joint ROM or anterior displacement.

Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research

The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.

The occipitoatlantal capsular ligaments are the primary stabilizers of the occipitoatlantal joint in the craniocervical junction: a finite element analysis

OBJECTIVE

There is contradictory evidence regarding the relative contribution of the key stabilizing ligaments of the occipitoatlantal (OA) joint. Cadaveric studies are limited by the nature and the number of injury scenarios that can be tested to identify OA stabilizing ligaments. Finite element (FE) analysis can overcome these limitations and provide valuable data in this area. The authors completed an FE analysis of 5 subject-specific craniocervical junction (CCJ) models to investigate the biomechanics of the OA joint and identify the ligamentous structures essential for stability.

METHODS

Isolated and combined injury scenarios were simulated under physiological loads for 5 validated CCJ FE models to assess the relative role of key ligamentous structures on OA joint stability. Each model was tested in flexion-extension, axial rotation, and lateral bending in various injury scenarios. Isolated ligamentous injury scenarios consisted of either decreasing the stiffness of the OA capsular ligaments (OACLs) or completely removing the transverse ligament (TL), tectorial membrane (TM), or alar ligaments (ALs). Combination scenarios were also evaluated.

RESULTS

An isolated OACL injury resulted in the largest percentage increase in all ranges of motion (ROMs) at the OA joint compared with the other isolated injuries. Flexion, extension, lateral bending, and axial rotation significantly increased by 12.4% ± 7.4%, 11.1% ± 10.3%, 83.6% ± 14.4%, and 81.9% ± 9.4%, respectively (p ≤ 0.05 for all). Among combination injuries, OACL+TM+TL injury resulted in the most consistent significant increases in ROM for both the OA joint and the CCJ during all loading scenarios. OACL+AL injury caused the most significant percentage increase for OA joint axial rotation.

CONCLUSIONS

These results demonstrate that the OACLs are the key stabilizing ligamentous structures of the OA joint. Injury of these primary stabilizing ligaments is necessary to cause OA instability. Isolated injuries of TL, TM, or AL are unlikely to result in appreciable instability at the OA joint.

A Formulation for Fluid Structure-Interactions in FEBio Using Mixture Theory

Many physiological systems involve strong interactions between fluids and solids, posing a signicant challenge when modeling biomechanics. The objective of this study was to implement a fluid-structure interaction (FSI) solver in the free, open-source finite element code FEBio (febio.org), that combined the existing solid mechanics and rigid body dynamics solver with a recently-developed computational fluid dynamics (CFD) solver. A novel Galerkin-based finite element FSI formulation was introduced based on mixture theory, where the FSI domain was described as a mixture of fluid and solid constituents that have distinct motions. The mesh was defined on the solid domain, specialized to have zero mass, negligible stiffness and zero frictional interactions with the fluid, whereas the fluid was modeled as isothermal and compressible. The mixture framework provided the foundation for evaluating material time derivatives in a material frame for the solid and in a spatial frame for the fluid. Similar to our recently reported CFD solver, our FSI formulation did not require stabilization methods to achieve good convergence, producing a compact set of equations and code implementation. The code was successfully verified against benchmark problems and an analytical solution for squeeze-film lubrication. It was validated against experimental measurements of the flow rate in a peristaltic pump, and illustrated using non-Newtonian blood flow through a bifurcated carotid artery with a thick arterial wall. The successful formulation and implementation of this FSI solver enhances the multiphysics modeling capabilities in FEBio relevant to the biomechanics and biophysics communities.

A Plugin Framework for Extending the Simulation Capabilities of FEBio

The FEBio software suite is a set of software tools for nonlinear finite element analysis in biomechanics and biophysics. FEBio employs mixture theory to account for the multiconstituent nature of biological materials, integrating the field equations for irreversible thermodynamics, solid mechanics, fluid mechanics, mass transport with reactive species, and electrokinetics. This communication describes the development and application of a new "plugin" framework for FEBio. Plugins are dynamically linked libraries that allow users to add new features and to couple FEBio with other domain-specific software applications without modifying the source code directly. The governing equations and simulation capabilities of FEBio are reviewed. The implementation, structure, use, and application of the plugin framework are detailed. Several example plugins are described in detail to illustrate how plugins enrich, extend, and leverage existing capabilities in FEBio, including applications to deformable image registration, constitutive modeling of biological tissues, coupling to an external software package that simulates angiogenesis using a discrete computational model, and a nonlinear reaction-diffusion solver. The plugin feature facilitates dissemination of new simulation methods, reproduction of published results, and coupling of FEBio with other domain-specific simulation approaches such as compartmental modeling, agent-based modeling, and rigid-body dynamics. We anticipate that the new plugin framework will greatly expand the range of applications for the FEBio software suite and thus its impact.