Subject-specific Geometry Research Articles

Modeling CSF circulation and the glymphatic system during infusion using subject specific intracranial pressures and brain geometries

BackgroundInfusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open question. Here we introduce a computational model that includes the glymphatic system and enables us to determine the importance of (1) brain geometry, (2) intracranial pressure, and (3) physiological parameters on the outcome of and response to an infusion test.MethodsWe implemented a seven-compartment multiple network porous medium model with subject specific geometries from MR images using the finite element library FEniCS. The model consists of the arterial, capillary and venous blood vessels, their corresponding perivascular spaces, and the extracellular space (ECS). Both subject specific brain geometries and subject specific infusion tests were used in the modeling of both healthy adults and iNPH patients. Furthermore, we performed a systematic study of the effect of variations in model parameters.ResultsBoth the iNPH group and the control group reached a similar steady state solution when subject specific geometries under identical boundary conditions was used in simulation. The difference in terms of average fluid pressure and velocity between the iNPH and control groups, was found to be less than 6% during all stages of infusion in all compartments. With subject specific boundary conditions, the largest computed difference was a 75% greater fluid speed in the arterial perivascular space (PVS) in the iNPH group compared to the control group. Changes to material parameters changed fluid speeds by several orders of magnitude in some scenarios. A considerable amount of the CSF pass through the glymphatic pathway in our models during infusion, i.e., 28% and 38% in the healthy and iNPH patients, respectively.ConclusionsUsing computational models, we have found the relative importance of subject specific geometries to be less important than individual differences in resistance as measured with infusion tests and model parameters such as permeability, in determining the computed pressure and flow during infusion. Model parameters are uncertain, but certain variations have large impact on the simulation results. The computations resulted in a considerable amount of the infused volume passing through the brain either through the perivascular spaces or the extracellular space.

Subject-Specific Geometry of FE Lumbar Spine Models for the Replication of Fracture Locations Using Dynamic Drop Tests.

For traumatic lumbar spine injuries, the mechanisms and influence of anthropometrical variation are not yet fully understood under dynamic loading. Our objective was to evaluate whether geometrically subject-specific explicit finite element (FE) lumbar spine models based on state-of-the-art clinical CT data combined with general material properties from the literature could replicate the experimental responses and the fracture locations via a dynamic drop tower-test setup. The experimental CT datasets from a dynamic drop tower-test setup were used to create anatomical details of four lumbar spine models (T12 to L5). The soft tissues from THUMS v4.1 were integrated by morphing. Each model was simulated with the corresponding loading and boundary conditions from the dynamic lumbar spine tests that produced differing injuries and injury locations. The simulations resulted in force, moment, and kinematic responses that effectively matched the experimental data. The pressure distribution within the models was used to compare the fracture occurrence and location. The spinal levels that sustained vertebral body fracture in the experiment showed higher simulation pressure values in the anterior elements than those in the levels that did not fracture in the reference experiments. Similarly, the spinal levels that sustained posterior element fracture in the experiments showed higher simulation pressure values in the vertebral posterior structures compared to those in the levels that did not sustain fracture. Our study showed that the incorporation of the spinal geometry and orientation could be used to replicate the fracture type and location under dynamic loading. Our results provided an understanding of the lumbar injury mechanisms and knowledge on the load thresholds that could be used for injury prediction with explicit FE lumbar spine models.

Breast simulation pipeline: From medical imaging to patient-specific simulations

BackgroundBreast-conserving surgery is the most acceptable operation for breast cancer removal from an invasive and psychological point of view. Before the surgical procedure, a preoperative MRI is performed in the prone configuration, while the surgery is achieved in the supine position. This leads to a considerable movement of the breast, including the tumor, between the two poses, complicating the surgeon's task. MethodsIn this work, a simulation pipeline allowing the computation of patient-specific geometry and the prediction of personalized breast material properties was put forward. Through image segmentation, a finite element model including the subject-specific geometry is established. By first computing an undeformed state of the breast, the geometrico-material model is calibrated by surface acquisition in the intra-operative stance. FindingsUsing an elastic corotational formulation, the patient-specific mechanical properties of the breast and skin were identified to obtain the best estimates of the supine configuration. The final results are a shape-fitting closest point residual of 4.00 mm for the mechanical parameters Ebreast=0.32 kPa and Eskin=22.72 kPa, congruent with the current state-of-the-art. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 5 to 30 min depending on the initial parameters, reaching a simulation speed of 20 s. To our knowledge, our model offers one of the best compromises between accuracy and speed. InterpretationSatisfactory results were obtained for the estimation of breast deformation from preoperative to intra-operative configuration. Furthermore, we have demonstrated the clinical feasibility of such applications using a simulation framework that aims at the smallest disturbance of the actual surgical pipeline.

An image-driven micromechanical approach to characterize multiscale remodeling in infarcted myocardium

Myocardial infarction (MI) is accompanied by the formation of a fibrotic scar in the left ventricle (LV) and initiates significant alterations in the architecture and constituents of the LV free wall (LVFW). Previous studies have shown that LV adaptation is highly individual, indicating that the identification of remodeling mechanisms post-MI demands a fully subject-specific approach that can integrate a host of structural alterations at the fiber-level to changes in bulk biomechanical adaptation at the tissue-level. We present an image-driven micromechanical approach to characterize remodeling, assimilating new biaxial mechanical data, histological studies, and digital image correlation data within an in-silico framework to elucidate the fiber-level remodeling mechanisms that drive tissue-level adaptation for each subject. We found that a progressively diffused collagen fiber structure combined with similarly disorganized myofiber architecture in the healthy region leads to the loss of LVFW anisotropy post-MI, offering an important tissue-level hallmark for LV maladaptation. In contrast, our results suggest that reductions in collagen undulation are an adaptive mechanism competing against LVFW thinning. Additionally, we show that the inclusion of subject-specific geometry when modeling myocardial tissue is essential for accurate prediction of tissue kinematics. Our approach serves as an essential step toward identifying fiber-level remodeling indices that govern the transition of MI to systolic heart failure. These indices complement the traditional, organ-level measures of LV anatomy and function that often fall short of early prognostication of heart failure in MI. In addition, our approach offers an integrated methodology to advance the design of personalized interventions, such as hydrogel injection, to reinforce and suppress native adaptive and maladaptive mechanisms, respectively, to prevent the transition of MI to heart failure. Statement of significanceBiomechanical and architectural adaptation of the LVFW remains a central, yet overlooked, remodeling process post-MI. Our study indicates the biomechanical adaptation of the LVFW post-MI is highly individual and driven by altered fiber network architecture and collective changes in collagen fiber content, undulation, and stiffness. Our findings demonstrate the possibility of using cardiac strains to infer such fiber-level remodeling events through in-silico modeling, paving the way for in-vivo characterization of multiscale biomechanical indices in humans. Such indices will complement the traditional, organ-level measures of LV anatomy and function that often fall short of early prognostication of heart failure in MI.

Body surface potential mapping detects early disease onset in plakophilin-2-pathogenic variant carriers.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a progressive inherited cardiac disease. Early detection of disease and risk stratification remain challenging due to heterogeneous phenotypic expression. The standard configuration of the 12 lead electrocardiogram (ECG) might be insensitive to identify subtle ECG abnormalities. We hypothesized that body surface potential mapping (BSPM) may be more sensitive to detect subtle ECG abnormalities. We obtained 67 electrode BSPM in plakophilin-2 (PKP2)-pathogenic variant carriers and control subjects. Subject-specific computed tomography/magnetic resonance imaging based models of the heart/torso and electrode positions were created. Cardiac activation and recovery patterns were visualized with QRS- and STT-isopotential map series on subject-specific geometries to relate QRS-/STT-patterns to cardiac anatomy and electrode positions. To detect early signs of functional/structural heart disease, we also obtained right ventricular (RV) echocardiographic deformation imaging. Body surface potential mapping was obtained in 25 controls and 42 PKP2-pathogenic variant carriers. We identified five distinct abnormal QRS-patterns and four distinct abnormal STT-patterns in the isopotential map series of 31/42 variant carriers. Of these 31 variant carriers, 17 showed no depolarization or repolarization abnormalities in the 12 lead ECG. Of the 19 pre-clinical variant carriers, 12 had normal RV-deformation patterns, while 7/12 showed abnormal QRS- and/or STT-patterns. Assessing depolarization and repolarization by BSPM may help in the quest for early detection of disease in variant carriers since abnormal QRS- and/or STT-patterns were found in variant carriers with a normal 12 lead ECG. Because electrical abnormalities were observed in subjects with normal RV-deformation patterns, we hypothesize that electrical abnormalities develop prior to functional/structural abnormalities in ARVC.

Fully automatic tracking of native glenohumeral kinematics from stereo-radiography

The current work introduces a system for fully automatic tracking of native glenohumeral kinematics in stereo-radiography sequences. The proposed method first applies convolutional neural networks to obtain segmentation and semantic key point predictions in biplanar radiograph frames. Preliminary bone pose estimates are computed by solving a non-convex optimization problem with semidefinite relaxations to register digitized bone landmarks to semantic key points. Initial poses are then refined by registering computed tomography-based digitally reconstructed radiographs to captured scenes, which are masked by segmentation maps to isolate the shoulder joint. A particular neural net architecture which exploits subject-specific geometry is also introduced to improve segmentation predictions and increase robustness of subsequent pose estimates. The method is evaluated by comparing predicted glenohumeral kinematics to manually tracked values from 17 trials capturing 4 dynamic activities. Median orientation differences between predicted and ground truth poses were 1.7∘ and 8.6∘ for the scapula and humerus, respectively. Joint-level kinematics differences were less than 2∘ in 65%, 13%, and 63% of frames for XYZ orientation DoFs based on Euler angle decompositions. Automation of kinematic tracking can increase scalability of tracking workflows in research, clinical, or surgical applications.

On the identification of hypoxic regions in subject-specific cerebral vasculature by combined CFD/MRI.

A long-time exposure to lack of oxygen (hypoxia) in some regions of the cerebrovascular system is believed to be one of the causes of cerebral neurological diseases. In the present study, we show how a combination of magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) can provide a non-invasive alternative for studying blood flow and transport of oxygen within the cerebral vasculature. We perform computer simulations of oxygen mass transfer in the subject-specific geometry of the circle of Willis. The computational domain and boundary conditions are based on four-dimensional (4D)-flow MRI measurements. Two different oxygen mass transfer models are considered: passive (where oxygen is treated as a dilute chemical species in plasma) and active (where oxygen is bonded to haemoglobin) models. We show that neglecting haemoglobin transport results in a significant underestimation of the arterial wall mass transfer of oxygen. We identified the hypoxic regions along the arterial walls by introducing the critical thresholds that are obtained by comparison of the estimated range of Damköhler number (Da ⊂ 〈9; 57〉) with the local Sherwood number. Finally, we recommend additional validations of the combined MRI/CFD approach proposed here for larger groups of subject- or patient-specific brain vasculature systems.

Tibial-fibular geometry and density variations associated with elevated bone strain and sex disparities in young active adults.

Tibial stress fracture is a common injury in runners and military personnel. Elevated bone strain is believed to be associated with the development of stress fractures and is influenced by bone geometry and density. The purpose of this study was to characterize tibial-fibular geometry and density variations in young active adults, and to quantify the influence of these variations on finite element-predicted bone strain. A statistical appearance model characterising tibial-fibular geometry and density was developed from computed tomography scans of 48 young physically active adults. The model was perturbed ±1 and 2 standard deviations along each of the first five principal components to create finite element models. Average male and female finite element models, controlled for scale, were also generated. Muscle and joint forces in running, calculated using inverse dynamics-based static optimization, were applied to the finite element models. The resulting 95th percentile pressure-modified von Mises strain (peak strain) and strained volume (volume of elements above 4000 με) were quantified. Geometry and density variations described by principal components resulted in up to 12.0% differences in peak strain and 95.4% differences in strained volume when compared to the average tibia-fibula model. The average female illustrated 5.5% and 41.3% larger peak strain and strained volume, respectively, when compared to the average male, suggesting that sexual dimorphism in bone geometry may indeed contribute to greater stress fracture risk in females. Our findings identified important features in subject-specific geometry and density associated with elevated bone strain that may have implications for stress fracture risk.

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 = 15MPa and Poisson's ratio = 0.475 yielded results most comparable to FEA, with peak pressure differences of 4.34 ± 1.98MPa (% 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.

Subject-Specific 3D Models to Investigate the Influence of Rehabilitation Exercises and the Twisted Structure on Achilles Tendon Strains

The Achilles tendon (AT) is the largest tendon of the human body and has a primary role in locomotor activities. The complex structure of the AT includes twisting of three sub-tendons, non-uniform tissue deformations and differential triceps surae muscle forces. The main aim of this study was to investigate the impact of commonly used rehabilitation exercises (walking on heels, walking on toes, unilateral heel rise, heel drop with extended knee and heel drop with the knee bent) and different twists on AT strains. 3D freehand ultrasound based subject-specific geometry and subject-specific muscle forces during different types of rehabilitation exercises were used to determine tendon strains magnitudes and differences in strains between the sub-tendons. In addition, three Finite Element models were developed to investigate the impact of AT twist. While walking on heels developed the lowest average strain, heel drop with knee bent exhibited the highest average strain. The eccentric heel drop resulted in higher peak and average strain, compared to concentric heel rise for all the three models. The isolated exercises (heel rise and heel drop) presented higher average strains compared to the functional exercises (walking tasks). The amount of twist influences the peak strains but not the average. Type I consistently showed highest peak strains among the five rehabilitation exercises. The ranking of the exercises based on the AT strains was independent of AT twist. These findings might help clinicians to prescribe rehabilitation exercises for Achilles tendinopathy based on their impact on the AT strains.

Tibiofemoral contact force differences between flat flexible and stable supportive walking shoes in people with varus-malaligned medial knee osteoarthritis: A randomized cross-over study.

To compare the effect of stable supportive to flat flexible walking shoes on medial tibiofemoral contact force (MTCF) in people with medial knee osteoarthritis and varus malalignment. This was a randomized cross-over study. Twenty-eight participants aged ≥50 years with medial knee osteoarthritis and varus malalignment were recruited from the community. Three-dimensional full-body motion, ground reaction forces and surface electromyograms from twelve lower-limb muscles were acquired during six speed-matched walking trials for flat flexible and stable supportive shoes, tested in random order. An electromyogram-informed neuromusculoskeletal model with subject-specific geometry estimated bodyweight (BW) normalized MTCF. Waveforms were analyzed using statistical parametric mapping with a repeated measures analysis of variance model. Peak MTCF, MTCF impulse and MTCF loading rates (discrete outcomes) were evaluated using a repeated measures multivariate analysis of variance model. Statistical parametric mapping showed lower MTCF in stable supportive compared to flat flexible shoes during 5-18% of stance phase (p = 0.001). For the discrete outcomes, peak MTCF and MTCF impulse were not different between the shoe styles. However, mean differences [95%CI] in loading impulse (-0.02 BW·s [-0.02, 0.01], p<0.001), mean loading rate (-1.42 BW·s-1 [-2.39, -0.45], p = 0.01) and max loading rate (-3.26 BW·s-1 [-5.94, -0.59], p = 0.02) indicated lower measure of loading in stable supportive shoes compared to flexible shoes. Stable supportive shoes reduced MTCF during loading stance and reduced loading impulse/rates compared to flat flexible shoes and therefore may be more suitable in people with medial knee osteoarthritis and varus malalignment. Australian and New Zealand Clinical Trials Registry (12619000622101).

Evaluation of anterior cruciate ligament surgical reconstruction through finite element analysis

Anterior cruciate ligament (ACL) tear is one of the most common knee injuries. The ACL reconstruction surgery aims to restore healthy knee function by replacing the injured ligament with a graft. Proper selection of the optimal surgery parameters is a complex task. To this end, we developed an automated modeling framework that accepts subject-specific geometries and produces finite element knee models incorporating different surgical techniques. Initially, we developed a reference model of the intact knee, validated with data provided by the Open Knee(s) project. This helped us evaluate the effectiveness of estimating ligament stiffness directly from MRI. Next, we performed a plethora of “what-if” simulations, comparing responses with the reference model. We found that (a) increasing graft pretension and radius reduces relative knee displacement, (b) the correlation of graft radius and tension should not be neglected, (c) graft fixation angle of 20^{circ } can reduce knee laxity, and (d) single-versus double-bundle techniques demonstrate comparable performance in restraining knee translation. In most cases, these findings confirm reported values from comparative clinical studies. The numerical models are made publicly available, allowing for experimental reuse and lowering the barriers for meta-studies. The modeling approach proposed here can complement orthopedic surgeons in their decision-making.

A parametric head geometry model accounting for variation among adolescent and young adult populations

Background and objectiveModeling the size and shape of human skull and scalp is essential for head injury assessment, design of helmets and head-borne equipment, and many other safety applications. Finite element (FE) head models are important tools to assess injury risks and design personal protective equipment. However, current FE head models are mainly developed based on the midsize male, failing to account for the significant morphological variation that exists in the skull and brain. The objective of this study was to develop a statistical head geometry model that accounts for size and shape variations among the adolescent and young adult population. MethodsTo represent subject-specific geometry using a homologous mesh, threshold-based segmentation of head CT scans of 101 subjects between 14 and 25 years of age was performed, followed by landmarking, mesh morphing, and projection. Skull and scalp statistical geometry models were then developed as functions of age, sex, stature, BMI, head length, head breadth, and tragion-to-top of head using generalized Procrustes analysis (GPA), principal component analysis (PCA) and multivariate regression analysis. ResultsThe statistical geometry models account for a high percentage of morphological variations in scalp geometry (R2=0.63), outer skull geometry (R2=0.66), inner skull geometry (R2=0.55), and skull thickness (error < 1 mm) ConclusionsSkull and scalp statistical geometry models accounts for size and shape variations among the adolescent and young adult population were developed as functions of subject covariates. These models may serve as the geometric basis to develop individualized head FE models for injury assessment and design of head-borne equipment.

Investigation of the Influence of Osteoporosis and Aging on Periprosthetic Femoral Fractures using Finite Element Analyses

With the increasing life expectancy and more active lifestyles of patients, the number of total hip replacements is constantly rising. Consequently, the incidence of periprosthetic femoral fractures (PFF) is also growing and facilitated by osteoporosis as well as advancing age of the patient. One way to prevent PFF is to optimize the treatment of individual patients, who are potentially at risk of osteoporotic fractures, by choosing the most suitable implant design for their subject specific circumstances. Thereby, revision surgeries due to PFF can be avoided. In this context appropriate models are necessary to predict fractures for aging and osteoporotic bone. On the basis of quantitative computed tomography (QCT) data subject specific finite element models with a heterogeneous material definition were created that can simulate PFF via element deletion. Statistical studies on the changes of bone quality with increasing patient age and developing osteoporosis were incorporated into the models. With statistical data, the bone mineral density (BMD) values measured in the QCT scans of the modelled femurs were varied, while the individual heterogeneous density distribution was preserved. The new density values were mapped onto the FE meshes of the femurs. This way, the femur was modelled to mirror the condition of the bone in different age groups and with different severity levels of osteoporosis in high age. The final models incorporate the subject specific geometry and density distribution of a patient's femur as well as the statistical density values resulting from aging and osteoporosis. The FE analyses revealed that a statistical change of bone density with aging leads to a relative small decrease in fracture load for periprosthetic fractures of a femur with a cementless endoprosthesis. However, for osteoporosis, the fracture load can differ by up to 56% with a cementless endoprosthesis, depending on the severity level of the disease.

Expediting Finite Element Analyses for Subject-Specific Studies of Knee Osteoarthritis: A Literature Review

Osteoarthritis (OA) is a degenerative disease that affects the synovial joints, especially the knee joint, diminishing the ability of patients to perform daily physical activities. Unfortunately, there is no cure for this nearly irreversible musculoskeletal disorder. Nowadays, many researchers aim for in silico-based methods to simulate personalized risks for the onset and progression of OA and evaluate the effects of different conservative preventative actions. Finite element analysis (FEA) has been considered a promising method to be developed for knee OA management. The FEA pipeline consists of three well-established phases: pre-processing, processing, and post-processing. Currently, these phases are time-consuming, making the FEA workflow cumbersome for the clinical environment. Hence, in this narrative review, we overviewed present-day trends towards clinical methods for subject-specific knee OA studies utilizing FEA. We reviewed studies focused on understanding mechanisms that initiate knee OA and expediting the FEA workflow applied to the whole-organ level. Based on the current trends we observed, we believe that forthcoming knee FEAs will provide nearly real-time predictions for the personalized risk of developing knee OA. These analyses will integrate subject-specific geometries, loading conditions, and estimations of local tissue mechanical properties. This will be achieved by combining state-of-the-art FEA workflows with automated approaches aided by machine learning techniques.

Are subject-specific models necessary to predict patellar tendon fatigue life? A finite element modelling study

Patellar tendinopathy is an overuse injury that occurs from repetitive loading of the patellar tendon in a scenario resembling that of mechanical fatigue. As such, fatigue-life estimates provide a quantifiable approach to assess tendinopathy risk and may be tabulated using nominal strain (NS) or finite element (FE) models with varied subject-specificity. We compared patellar tendon fatigue-life estimates from NS and FE models of twenty-nine athletes performing countermovement jumps with subject-specific versus generic geometry and material properties. Subject-specific patellar tendon material properties and geometry were obtained using a data collection protocol of dynamometry, ultrasound, and magnetic resonance imaging. Three FE models were created for each subject, with: subject-specific (hyperelastic) material properties and geometry, subject-specific material properties and generic geometry, and generic material properties and subject-specific geometry. Four NS models were created for each subject, with: subject-specific (linear elastic) material properties and moment arm, generic material properties and subject-specific moment arm, subject-specific material properties and generic moment arm, and generic material properties and moment arm. NS- and FE-modelled fatigue-life estimates with generic material properties were poorly correlated with their subject-specific counterparts (r2 ≤0.073), while all NS models overestimated fatigue life compared to the subject-specific FE model (r2 ≤0.223). Furthermore, FE models with generic tendon geometry were unable to accurately represent the heterogeneous strain distributions found in the subject-specific FE models or those with generic material properties. These findings illustrate the importance of incorporating subject-specific material properties and FE-modelled strain distributions into fatigue-life estimations.

Torsion Tool: An automated tool for personalising femoral and tibial geometries in OpenSim musculoskeletal models

Common practice in musculoskeletal modelling is to use scaled musculoskeletal models based on a healthy adult, but this does not consider subject-specific geometry, such as tibial torsion and femoral neck-shaft and anteversion angles (NSA and AVA). The aims of this study were to (1) develop an automated tool for creating OpenSim models with subject-specific tibial torsion and femoral NSA and AVA, (2) evaluate the femoral component, and (3) release the tool open-source.The Torsion Tool (https://simtk.org/projects/torsiontool) is a MATLAB-based tool that requires an individual’s tibial torsion, NSA and AVA estimates as input and rotates corresponding bones and associated muscle points of a generic musculoskeletal model. Performance of the Torsion Tool was evaluated comparing femur bones as personalised with the Torsion Tool and scaled generic femurs with manually segmented bones as golden standard for six typically developing children and thirteen children with cerebral palsy.The tool generated femur geometries closer to the segmentations, with lower maximum (−19%) and root mean square (−18%) errors and higher Jaccard indices (+9%) compared to generic femurs. Furthermore, the tool resulted in larger improvements for participants with higher NSA and AVA deviations.The Torsion Tool allows an automatic, fast, and user-friendly way of personalising femoral and tibial geometry in an OpenSim musculoskeletal model. Personalisation is expected to be particularly relevant in pathological populations, as will be further investigated by evaluating the effects on simulation outcomes.

The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model – a sensitivity study

Outputs of musculoskeletal models should be considered probabilistic rather than deterministic as they are affected by inaccuracies and estimations associated with the development of the model. One of these uncertainties being critical for modeling arises from the determination of the muscles' line of action and the physiological cross-sectional area. Therefore, the aim of this study was to evaluate the outcome sensitivity of model predictions from a musculoskeletal hand model in comparison to the uncertainty of these input parameters. For this purpose, the kinematics and muscle activities of different hand movements (abduction of the fingers, abduction of the thumb, and flexion of the thumb) were recorded. One thousand simulations were calculated for each movement using the Latin hypercube sampling method with a corresponding variation of the muscle origin/insertion points and the cross-sectional area. Comparing the standard hand to simulations incorporating uncertainties of input parameters shows no major deviations in on- and off-set time point of muscle activities. About 60% of simulations are located within a ± 30% interval around the standard model concerning joint reaction forces. The comparison with the variation of the input data leads to the conclusion that the standard hand model is able to provide not over-scattered outcomes and, therefore, can be considered relatively stable. These results are of practical importance to the personalization of a musculoskeletal model with subject-specific bone geometries and hence changed muscle line of action.

Load Distribution in the Lumbar Spine During Modeled Compression Depends on Lordosis.

Excessive or incorrect loading of lumbar spinal structures is commonly assumed as one of the factors to accelerate degenerative processes, which may lead to lower back pain. Accordingly, the mechanics of the spine under medical conditions, such as scoliosis or spondylolisthesis, is well-investigated. Treatments via both conventional therapy and surgical methods alike aim at restoring a “healthy” (or at least pain-free) load distribution. Yet, surprisingly little is known about the inter-subject variability of load bearings within a “healthy” lumbar spine. Hence, we utilized computer tomography data from 28 trauma-room patients, whose lumbar spines showed no visible sign of degeneration, to construct simplified multi-body simulation models. The subject-specific geometries, measured by the corresponding lumbar lordosis (LL) between the endplates of vertebra L1 and the sacrum, served as ceteris paribus condition in a standardized forward dynamic compression procedure. Further, the influence of stimulating muscles from the M. multifidus group was assessed. For the range of available LL from 28 to 66°, changes in compressive and shear forces, bending moments, as well as facet joint forces between adjacent vertebrae were calculated. While compressive forces tended to decrease with increasing LL, facet forces were tendentiously increasing. Shear forces decreased between more cranial vertebrae and increased between more caudal ones, while bending moments remained constant. Our results suggest that there exist significant, LL-dependent variations in the loading of “healthy” spinal structures, which should be considered when striving for individually appropriate therapeutic measures.

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions. Statement of SignificanceThe study presents a method to calibrate and evaluate subject-specific finite element brain models using a combination of advanced magnetic resonance imaging (MRI) data. The imaging data is acquired in human volunteers and includes anatomical MRI, magnetic resonance elastography (MRE), and tagged MRI to generate subject-specific geometry, calibrate subject-specific material properties, and evaluate simulation response using subject-specific brain deformation. This dataset of MRE and tagged MRI allows for a unique evaluation of whether material properties from MRE can be used to create biofidelic computational models of the human brain. The study develops a calibration procedure to readily calculate linear viscoelastic material parameters from MRE data and then provides a sensitivity study of the effect of mechanical heterogeneity of the brain on simulation response. The calibrated computational models are used to simulate each subject's tagged MRI experiment; the results show good agreement between the simulated and experimental strain fields. The presented study and results will be informative in guiding the calibration of subject-specific computational brain model from experimental MRE data. The processed MRI, MRE, and tagged MRI data are publicly available at https://www.nitrc.org/projects/bbir/.