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Giordano, C. (2017). Development of an Anisotropic Finite Element Head Model for Traumatic Brain Injury Prediction. (Doctoral dissertation). KTH Royal Institute of Technology
Open this publication in new window or tab >>Development of an Anisotropic Finite Element Head Model for Traumatic Brain Injury Prediction
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
Keywords
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:nbn:se:kth:diva-207797 (URN)978-91-7729-357-6 (ISBN)
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
Beillas, P., Wang, X., Lafon, Y., Frechede, B., Janak, T., Dupeux, T., . . . et al, . (2017). PIPER EU Project Final publishable summary.
Open this publication in new window or tab >>PIPER EU Project Final publishable summary
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2017 (English)Report (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-259186 (URN)
Note

QC 20190912

Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-11-14Bibliographically approved
Giordano, C. & Kleiven, S. (2016). Development of a 3-year-old child FE head model, continuously scalable from 1.5-to 6-year-old. In: 2016 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury: . Paper presented at 2016 International Research Council on the Biomechanics of Injury, IRCOBI 2016, 14 September 2016 through 16 September 2016 (pp. 288-302). International Research Council on the Biomechanics of Injury
Open this publication in new window or tab >>Development of a 3-year-old child FE head model, continuously scalable from 1.5-to 6-year-old
2016 (English)In: 2016 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury, International Research Council on the Biomechanics of Injury , 2016, p. 288-302Conference paper, Published paper (Refereed)
Abstract [en]

This study summarised efforts in developing a 3-year-old FE head model, continuously scalable in the range 1.5-to 6-year-old. The FE models were transformed into one another using nonlinear scaling driven by control points corresponding to anthropometric dimensions. Procedures to mimic age-specific structural changes occurring during the paediatric development were implemented by means of transition of elements. The performances of the head models were verified on drop and compressive tests available from the literature. A stable and experimentally well-correlated family of FE models in the range 1.5-to 6-year-old was created.

Place, publisher, year, edition, pages
International Research Council on the Biomechanics of Injury, 2016
Keywords
Child head, Child modeling, Finite element analysis, Morphology, Scaling procedure, Biomechanics, Pediatrics, Anthropometric dimensions, Child models, Compressive tests, Control point, Fe head models, Non-linear scaling, Scaling procedures, Finite element method
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-201986 (URN)2-s2.0-84996843078 (Scopus ID)
Conference
2016 International Research Council on the Biomechanics of Injury, IRCOBI 2016, 14 September 2016 through 16 September 2016
Note

QC 20170303

Available from: 2017-03-03 Created: 2017-03-03 Last updated: 2017-03-03Bibliographically approved
Giordano, C. & Kleiven, S. (2016). Development of an Unbiased Validation Protocol to Assess the Biofidelity of Finite Element Head Models used in Prediction of Traumatic Brain Injury. In: SAE Technical Papers: . Paper presented at 60th SAE Stapp Car Crash Conference, STAPP 2016; Hyatt Regency Washington on Capitol Hill, Washington; United States, 7 November 2016 through 9 November 2016. SAE International (November)
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: SAE Technical Papers, SAE International , 2016, no NovemberConference paper, Published paper (Refereed)
Abstract [en]

This study describes a method to identify laboratory test procedures and impact response requirements suitable for assessing the biofidelity of finite element head models used in prediction of traumatic brain injury. The selection of the experimental data and the response requirements were result of a critical evaluation based on the accuracy, reproducibility and relevance of the available experimental data. A weighted averaging procedure was chosen in order to consider different contributions from the various test conditions and target measurements based on experimental error. According to the quality criteria, 40 experimental cases were selected to be a representative dataset for validation. Based on the evaluation of response curves from four head finite element models, CORA was chosen as a quantitative method to compare the predicted time history response to the measured data. Optimization of the CORA global settings led to the recommendation of performing curve comparison 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 (DMAX = 0.12 for intracranial pressure, DMAX = 0.40 for brain motion and DMAX = 0.25 for brain deformation). Finally, bigger penalization of ratings was assigned to curves with fundamentally incorrect shape compared to those having inaccuracies in amplitude or time shift (cubic vs linear). This rigorous approach is necessary to ensure confidence in the model results and progress in the usage of finite element head models for traumatic brain injury prediction. 

Place, publisher, year, edition, pages
SAE International, 2016
Keywords
Biofidelity, Finite Element (FE) Head Model, Model Validation, Traumatic Brain Injury (TBI), Accidents, Brain, Curve fitting, Deformation, Forecasting, Finite element head models, Head model, Intra-cranial pressure, Motion and deformations, Time history response, Traumatic Brain Injuries, Finite element method
National Category
Applied Mechanics
Identifiers
urn:nbn:se:kth:diva-246617 (URN)10.4271/2016-22-0013 (DOI)2-s2.0-85059772915 (Scopus ID)
Conference
60th SAE Stapp Car Crash Conference, STAPP 2016; Hyatt Regency Washington on Capitol Hill, Washington; United States, 7 November 2016 through 9 November 2016
Note

QC 20190614

Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-14Bibliographically approved
Giordano, C. & Kleiven, S. (2014). Connecting Fractional Anisotropy from Medical Images with Mechanical Anisotropy of a Hyperviscoelastic Fibre-reinforced Constitutive Model for Brain Tissue. Journal of the Royal Society Interface, 11(91), 20130914
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.

Keywords
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
Giordano, C. & Kleiven, S. (2014). Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling. Stapp Car Crash Journal, 58
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.

Keywords
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
Giordano, C. & Kleiven, S. (2014). Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling. Paper presented at 10 November 2014 through 12 November 2014. 58th SAE Stapp Car Crash Conference, STAPP 2014
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: 58th SAE Stapp Car Crash Conference, STAPP 2014Article 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. 

Place, publisher, year, edition, pages
SAE International, 2014
Keywords
Anisotropy, Axonal strain, Brain material properties, Finite element analysis, Injury predictor, Traumatic brain injury, Accidents, Brain, Bromine compounds, Damage detection, Hybrid integrated circuits, Regression analysis, Risk assessment, Area under the ROC curve, Logistic regression analysis, Maximum principal strain, Mild traumatic brain injuries, Receiver operating characteristic curve analysis, Statistical correlation, Traumatic Brain Injuries, Finite element method
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:kth:diva-246647 (URN)10.4271/2014-22-0002 (DOI)2-s2.0-85059447238 (Scopus ID)
Conference
10 November 2014 through 12 November 2014
Note

QC 20190619

Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically approved
Giordano, C., Cloots, R. J., van Dommelen, J. A. & Kleiven, S. (2014). The influence of anisotropy on brain injury prediction. Journal of Biomechanics, 47(5), 1052-1059
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.

Keywords
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0569-5118

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