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Development of an Anisotropic Finite Element Head Model for Traumatic Brain Injury Prediction
KTH, School of Technology and Health (STH), Medical Engineering, Neuronic Engineering. (Neuronic Engineering)ORCID iD: 0000-0002-0569-5118
Number of Authors: 1
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traumatic brain injury (TBI) is a worldwide health care problem with very high associatedmorbidity and mortality rates. In particular, the diagnosis of TBI is challenging: symptomsoverlap with other pathologies and the injury is typically not visible with conventionalneuroimaging techniques.Finite element (FE) head models can provide valuable insight into uncovering themechanism underlying brain damage. These models enable the calculation of tissue loadsand deformation patterns, which are thought to be associated with the injury. Measuresbased on tissue strain or invariants of the strain tensor are used as injury predictors and riskinjury curves can be inferred to establish the tolerance of the human head to external loads.However, while in-vitro research shows that the vulnerability to injury is due to highlyorganized structure in white matter tracts, the majority of the current FE models model thebrain as isotropic and homogenous. The deformation of white matter tracts is not calculated.The aim of this doctoral thesis was to incorporate the effects of inhomogeneity andanisotropy of brain tissue into injury analysis. Based on in-vitro experimental evidence, thestrain in the direction of the axons (axonal strain) was proposed as a new, more anatomicallyrelevant, injury predictor. The initial hypothesis to investigate was that an FE anisotropichead model is a better tool to represent TBI because it is more biofidelic in describing thelocal mechanism of axonal impairment.The studies reported in this thesis describe a method for implementing the orientation of thewhite matter tracts in an anisotropic constitutive law for FE modeling. Results from thestudies suggested that the anisotropy of the brain significantly affected the injury predictionsof an FE head model. For an injury dataset from the American National Football League, thepeak of axonal strain - MAS - was found to be a better predictor of injury than isotropic localor global predictors. Finally, based on 27 cases of intracranial pressure, relative skull-brainmotion and brain deformation, the introduction of the brain anisotropy in the FE modelpartially enhanced the biofidelity of the simulations. However, given that the enhancementin biofidelity was not major, it was concluded that further research is necessary forunderstanding the relationship between tissue-level loading and axonal injury.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. , p. 60
Series
TRITA-STH : report, ISSN 1653-3836 ; 2017:5
Keyword [en]
Axonal Strain, Brain Anisotropy, Traumatic Brain Injury, Finite Element Analysis, Brain Tissue, Constitutive Modeling.
National Category
Engineering and Technology
Research subject
Medical Technology
Identifiers
URN: urn:nbn:se:kth:diva-207797ISBN: 978-91-7729-357-6 (print)OAI: oai:DiVA.org:kth-207797DiVA, id: diva2:1098414
Public defence
2017-06-02, T2, Hälsovägen 11C, Huddinge, 13:00 (English)
Opponent
Supervisors
Note

QC 20170524

Available from: 2017-05-24 Created: 2017-05-24 Last updated: 2017-05-24Bibliographically approved
List of papers
1. Connecting Fractional Anisotropy from Medical Images with Mechanical Anisotropy of a Hyperviscoelastic Fibre-reinforced Constitutive Model for Brain Tissue
Open this publication in new window or tab >>Connecting Fractional Anisotropy from Medical Images with Mechanical Anisotropy of a Hyperviscoelastic Fibre-reinforced Constitutive Model for Brain Tissue
2014 (English)In: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 11, no 91, p. 20130914-Article in journal (Refereed) Published
Abstract [en]

Brain tissue modelling has been an active area of research for years. Brain matter does not follow the constitutive relations for common materials and loads applied to the brain turn into stresses and strains depending on tissue local morphology. In this work, a hyperviscoelastic fibre-reinforced anisotropic law is used for computational brain injury prediction. Thanks to a fibrere-inforcement dispersion parameter, this formulation accounts for anisotropic features and heterogeneities of the tissue owing to different axon alignment. The novelty of the work is the correlation of the material mechanical anisotropy with fractional anisotropy (FA) from diffusion tensor images. Finite-element (FE) models are used to investigate the influence of the fibre distribution for different loading conditions. In the case of tensile-compressive loads, the comparison between experiments and simulations highlights the validity of the proposed FA-k correlation. Axon alignment affects the deformation predicted by FE models and, when the strain in the axonal direction is large with respect to the maximum principal strain, decreased maximum deformations are detected. It is concluded that the introduction of fibre dispersion information into the constitutive law of brain tissue affects the biofidelity of the simulations.

Keyword
Brain Tissue, Constitutive Modelling, Fibre Dispersion, Anisotropy
National Category
Medical Engineering
Identifiers
urn:nbn:se:kth:diva-134228 (URN)10.1098/rsif.2013.0914 (DOI)000332384600011 ()2-s2.0-84891897481 (Scopus ID)
Note

QC 20140131

Available from: 2013-11-27 Created: 2013-11-20 Last updated: 2017-05-29Bibliographically approved
2. The influence of anisotropy on brain injury prediction
Open this publication in new window or tab >>The influence of anisotropy on brain injury prediction
2014 (English)In: Journal of Biomechanics, ISSN 0021-9290, E-ISSN 1873-2380, Vol. 47, no 5, p. 1052-1059Article in journal (Refereed) Published
Abstract [en]

Traumatic Brain Injury (TBI) occurs when a mechanical insult produces damage to the brain and disrupts its normal function. Numerical head models are often used as tools to analyze TBIs and to measure injury based on mechanical parameters. However, the reliability of such models depends on the incorporation of an appropriate level of structural detail and accurate representation of the material behavior. Since recent studies have shown that several brain regions are characterized by a marked anisotropy, constitutive equations should account for the orientation-dependence within the brain. Nevertheless, in most of the current models brain tissue is considered as completely isotropic. To study the influence of the anisotropy on the mechanical response of the brain, a head model that incorporates the orientation of neural fibers is used and compared with a fully isotropic model. A simulation of a concussive impact based on a sport accident illustrates that significantly lowered strains in the axonal direction as well as increased maximum principal strains are detected for anisotropic regions of the brain. Thus, the orientation-dependence strongly affects the response of the brain tissue. When anisotropy of the whole brain is taken into account, deformation spreads out and white matter is particularly affected. The introduction of local axonal orientations and fiber distribution into the material model is crucial to reliably address the strains occurring during an impact and should be considered in numerical head models for potentially more accurate predictions of brain injury.

Keyword
Traumatic Brain Injury (TBI), Diffuse Axonal Injury (DAI), Anisotropy, Head model, Finite Element Method (FEM)
National Category
Biophysics
Identifiers
urn:nbn:se:kth:diva-145282 (URN)10.1016/j.jbiomech.2013.12.036 (DOI)000334088000017 ()2-s2.0-84894686231 (Scopus ID)
Note

QC 20140515

Available from: 2014-05-15 Created: 2014-05-15 Last updated: 2017-05-29Bibliographically approved
3. Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling
Open this publication in new window or tab >>Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling
2014 (English)In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 58Article in journal (Refereed) Published
Abstract [en]

Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI.Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.

Keyword
Finite element analysis, injury predictor, brain material properties, anisotropy, traumatic brain injury, axonal strain
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-158021 (URN)2-s2.0-84942820205 (Scopus ID)
Note

QC 20150407

Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2017-05-29Bibliographically approved
4. Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subjectvariability
Open this publication in new window or tab >>Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subjectvariability
2017 (English)In: Biomechanics and Modeling in Mechanobiology, ISSN 1617-7959, E-ISSN 1617-7940Article in journal (Refereed) Published
Abstract [en]

Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm3 volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.

Place, publisher, year, edition, pages
Springer, 2017
Keyword
Axonal strain; Brain anisotropy; Finite element analysis; Traumatic brain injury
National Category
Engineering and Technology
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-207792 (URN)10.1007/s10237-017-0887-5 (DOI)000405489600012 ()2-s2.0-85013659547 (Scopus ID)
Note

QC 20170529

Available from: 2017-05-24 Created: 2017-05-24 Last updated: 2017-08-08Bibliographically approved
5. Development of an Unbiased Validation Protocol to Assess the Biofidelity of Finite Element Head Models used in Prediction of Traumatic Brain Injury
Open this publication in new window or tab >>Development of an Unbiased Validation Protocol to Assess the Biofidelity of Finite Element Head Models used in Prediction of Traumatic Brain Injury
2016 (English)In: Stapp Car Crash Journal, ISSN 1532-8546, Vol. 60, p. 363-471Article in journal (Refereed) Published
Abstract [en]

This study describes a method to identify laboratory test procedures and impact response requirements suitablefor assessing the biofidelity of finite element head models used in prediction of traumatic brain injury. The selection of theexperimental data and the response requirements were result of a critical evaluation based on the accuracy, reproducibility andrelevance of the available experimental data. A weighted averaging procedure was chosen in order to consider differentcontributions from the various test conditions and target measurements based on experimental error. According to the qualitycriteria, 40 experimental cases were selected to be a representative dataset for validation. Based on the evaluation of responsecurves from four head finite element models, CORA was chosen as a quantitative method to compare the predicted time historyresponse to the measured data. Optimization of the CORA global settings led to the recommendation of performing curvecomparison on a fixed time interval of 0-30 ms for intracranial pressure and at least 0-40 ms for brain motion and deformation.The allowable maximum time shift was adjusted depending on the shape of the experimental curves (􀜦􀯆􀮺􀯑􀀃􀀃= 0.12 forintracranial pressure, 􀜦􀯆􀮺􀯑 = 0.40 for brain motion and 􀜦􀯆􀮺􀯑 = 0.25 for brain deformation). Finally, bigger penalization ofratings was assigned to curves with fundamentally incorrect shape compared to those having inaccuracies in amplitude or timeshift (cubic vs linear). This rigorous approach is necessary to ensure confidence in the model results and progress in the usage offinite element head models for traumatic brain injury prediction.

Keyword
Model Validation, Finite Element (FE) Head Model, Traumatic Brain Injury (TBI), Biofidelity
National Category
Engineering and Technology
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-207793 (URN)
Note

QC 20170530

Available from: 2017-05-24 Created: 2017-05-24 Last updated: 2017-06-02Bibliographically approved

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