scholarly journals Injury Biomechanics of a Child’s Head: Problems, Challenges and Possibilities with a New aHEAD Finite Element Model

2020 ◽  
Vol 10 (13) ◽  
pp. 4467
Author(s):  
Johannes Wilhelm ◽  
Mariusz Ptak ◽  
Fábio A. O. Fernandes ◽  
Konrad Kubicki ◽  
Artur Kwiatkowski ◽  
...  
Keyword(s):  
Finite Element ◽  
Brain Injury ◽  
White Matter ◽  
Numerical Models ◽  
Material Models ◽  
Detailed Model ◽  
Hexahedral Elements ◽  
The Impact ◽  
The Brain ◽  
Brain Tissues

Traumatic brain injury (TBI) is a major public health problem among children. The predominant causes of TBI in young children are motor vehicle accidents, firearm incidents, falls, and child abuse. The limitation of in vivo studies on the human brain has made the finite element modelling an important tool to study brain injury. Numerical models based on the finite element approach can provide valuable data on biomechanics of brain tissues and help explain many pathological conditions. This work reviews the existing numerical models of a child’s head. However, the existing literature is very limited in reporting proper geometric representation of a small child’s head. Therefore, an advanced 2-year-old child’s head model, named aHEAD 2yo (aHEAD: advanced Head models for safety Enhancement And medical Development), has been developed, which advances the state-of-the-art. The model is one of the first published in the literature, which entirely consists of hexahedral elements for three-dimensional (3D) structures of the head, such as the cerebellum, skull, and cerebrum with detailed geometry of gyri and sulci. It includes cerebrospinal fluid as Smoothed Particle Hydrodynamics (SPH) and a detailed model of pressurized bringing veins. Moreover, the presented review of the literature showed that material models for children are now one of the major limitations. There is also no unambiguous opinion as to the use of separate materials for gray and white matter. Thus, this work examines the impact of various material models for the brain on the biomechanical response of the brain tissues during the mechanical loading described by Hardy et al. The study compares the inhomogeneous models with the separation of gray and white matter against the homogeneous models, i.e., without the gray/white matter separation. The developed model along with its verification aims to establish a further benchmark in finite element head modelling for children and can potentially provide new insights into injury mechanisms.

Investigation of Stress Wave Propagation in Brain Tissues Through the Use of Finite Element Method

2010 ◽  
Author(s):  
Biaobiao Zhang ◽  
W. Steve Shepard ◽  
Candace L. Floyd
Keyword(s):  
Finite Element ◽  
Wave Propagation ◽  
Brain Injury ◽  
Stress Wave ◽  
Brain Research ◽  
Complex Structure ◽  
Stress Wave Propagation ◽  
Wave Loads ◽  
The Impact ◽  
The Brain

Because axons serve as the conduit for signal transmission within the brain, research related to axon damage during brain injury has received much attention in recent years. Although myelinated axons appear as a uniform white matter, the complex structure of axons has not been thoroughly considered in the study of fundamental structural injury mechanisms. Most axons are surrounded by an insulating sheath of myelin. Furthermore, hollow tube-like microtubules provide a form of structural support as well as a means for transport within the axon. In this work, the effects of microtubule and its surrounding protein mediums inside the axon structure are considered in order to obtain a better understanding of wave propagation within the axon in an attempt to make progress in this area of brain injury modeling. By examining axial wave propagation using a simplified finite element model to represent microtubule and its surrounding proteins assembly, the impact caused by stress wave loads within the brain axon structure can be better understood. Through conducting a transient analysis as the wave propagates, some important characteristics relative to brain tissue injuries are studied.

The Parametric Studies of Brain Materials in the Analysis of Head Impact

2006 ◽  
Author(s):  
Dalong Li ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Finite Element ◽  
Dynamic Response ◽  
Dynamic Behavior ◽  
Material Properties ◽  
Material Behavior ◽  
Material Models ◽  
Material Parameters ◽  
Multiple Materials ◽  
The Brain ◽  
Brain Tissues

Crash analysis and head injury biomechanics are very important fields in biomedical research due to the devastating consequences of traumatic brain injuries (TBI). Complex geometry and constitutive models of multiple materials can be combined with the loading conditions in finite element head model to study the dynamic behavior of brain and the TBI. In such a modeling, the proper regional material properties of brain tissues are important. Brain tissues material properties have not been finally determined by experiments, and large variations in the test data still exist and the data is very much situation-dependent. Therefore, parametric analysis should be performed to study the relationship between the material properties and the brain response. The main purpose of presenting this paper is to identify the influence of material constitutive properties on brain impact response, to search for an improved material model and to arrive at a better correlation between the finite element model and the cadaver tests data. In this paper a 3-D nonlinear finite element method will be used to study the dynamic response of the human head under dynamic loading. The finite element formulation includes detailed model of the skull, brain, cerebral-spinal fluid (CSF), dura mater, pia mater, falx and tentorium membranes. The brain is modeled as linear viscoelastic material, whereas linear elastic material behavior is assumed for all the other tissue components. The proper contact and compatibility conditions between different components have been implemented in the modeling procedure. The results for the direct frontal impacts will be shown for three groups of material parameters. The parametrical analysis of tissue material models allows to examines the accuracy of three different set of material parameters for brain in a comparison with the prediction of the head dynamic response of Nahum's human cadaver direct impact experiment. Three sets of suggested material parameters are examined. It is concluded that although all three groups of material models will follow the dynamic behavior of the head and brain behavior, but the parametric data considered in this paper have a closer resemblance to the experimental behavior.

A Finite Element Model for Estimating Axonal Damage in Traumatic Brain Injury

2012 ◽  
Author(s):  
Rika M. Wright ◽  
K. T. Ramesh
Keyword(s):  
Traumatic Brain Injury ◽  
Finite Element ◽  
Brain Injury ◽  
White Matter ◽  
Finite Element Model ◽  
Axonal Injury ◽  
Diffuse Axonal Injury ◽  
Element Model ◽  
Cellular Level ◽  
The Brain

There has been an ongoing effort to reduce the occurrence of sports-related traumatic brain injury. These injuries are caused by an impact to the head and often lead to the damage of neural axons in the brain. This type of damage is classified as diffuse axonal injury (DAI) or traumatic axonal injury (TAI) [1]. One of the difficulties in studying the progression of axonal injury is that the structural signature of DAI cannot be readily visualized with conventional medical imaging modalities since the damage occurs at the cellular level [2]. This also makes the injury difficult to diagnose. Many researchers have turned to finite element (FE) models to study the development of diffuse axonal injury. FE models provide a means for observing the mechanical process of injury development from the loads to the head at the macroscale to the damage that results at the cellular level. However, for a finite element model to be a viable tool for studying DAI, the model must be able to accurately represent the behavior of the brain tissue, and it must be able to accurately predict injury. In this work, we address both of these issues in an effort to improve the material models and injury criteria used in current FE models of TBI. We represent the white matter with an anisotropic, hyper-viscoelastic constitutive model, incorporate the microstructure of the white matter through the use of diffusion tensor imaging (DTI), and estimate injury using an axonal strain injury (ASI) criterion (Figure 1). We also develop a novel method to quantify the degree of axonal damage in the fiber tracts of the brain.

Comparison of Brain Tissue Material Finite Element Models Based on Threshold for Traumatic Brain Injury

2016 ◽  
Author(s):  
Ashkan Eslaminejad ◽  
Hesam Sarvghad-Moghaddam ◽  
Asghar Rezaei ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Traumatic Brain Injury ◽  
Finite Element ◽  
Brain Injury ◽  
Dynamic Response ◽  
Brain Tissue ◽  
Head Model ◽  
Fe Modeling ◽  
Material Models ◽  
The Brain ◽  
Tissue Material

Blast traumatic brain injury (bTBI) may happen due to sudden blast and high-frequency loads. Due to the moral issues and the burden of experimental approaches, using computational methods such as finite element analysis (FEA) can be effective. Several finite element studies have focused on the effects of TBI to anticipate and understand the brain dynamic response. One of the most important factors in every FEA study of bTBI is the accurate modeling of brain tissue material properties. The main goal of this study is a comparison of different brain tissue constitutive models to understand the dynamic response of brain under an identical blast load. The multi-material FE modeling of the human head has several limitations such as its complexity and consequently high computational costs. Therefore, a spherical head model is modeled which suggests more straightforward observation/understanding of the FE modeling of skull (solid), CSF (fluid), and the brain tissue. Three different material models are considered for the brain tissue, namely hyperelastic, viscoelastic, and hyperviscoelastic. Brain dynamic responses are studied in terms of the head kinematics (linear acceleration), intracranial pressure (ICP), shear stress, and maximum mechanical strain. Our results showed that the hyperelastic model predicts larger ICP and shear than other constitutive brain tissue models. However, all material models predicted similar shear strain and head accelerations.

BIOMECHANICAL ANALYSIS OF OCCUPANT’S BRAIN RESPONSE AND INJURY IN VEHICLE INTERIOR SECOND IMPACT UTILIZING A REFINED HEAD FINITE ELEMENT MODEL

2017 ◽  
Vol 17 (07) ◽  
pp. 1740018
Author(s):  
ZHENGWEI MA ◽  
LELE JING ◽  
JINLUN WANG ◽  
JIQING CHEN ◽  
FENGCHONG LAN
Keyword(s):  
Finite Element ◽  
Brain Injury ◽  
Side Impact ◽  
Positive Pressure ◽  
Head Model ◽  
Vertical Angle ◽  
Extreme Stress ◽  
Horizontal Angle ◽  
The Impact ◽  
The Brain

In vehicle side collisions, traumatic brain injury caused by the impact between occupant’s head and the interior parts of A or B pillar is a major reason of death and disability. In order to analyze the biomechanical response and injury mechanism of occupant’s brain in side collisions, a refined finite element head model representing the 50th percentile Chinese male was developed. Its improvements of biofidelity comparing to the original head model were illustrated through model simulation against the same post mortem human subjects test. Based on the refined head model, the brain biomechanical responses and injuries in the side impact with interior parts of A pillar and B pillar were analyzed according to FMVSS 201U, and the influences of different impact locations and directions were investigated. The results showed that the brain tissues on impact side sustained positive pressure and those on the opposite side experienced negative pressure. The transmission of pressure wave was easy to cause brain concussion and other diffuse brain injuries. The intracranial pressure distribution exhibited a typical pattern of contrecoup injury. The extreme stress concentration in the junction area of the cerebrum, cerebellum and brain stem could lead to focal injury such as brain contusion and laceration. Moreover, the impact injury of A pillar was more serious than that of B pillar, which was consistent with the traffic injury statistics that the head injury in oblique side collisions was more serious than that of vertical side collisions. Therefore, the interior parts of A pillar should be designed to absorb more energy than those of B pillar under the same conditions. In addition, the severity of brain injury is more sensitive to the variation of the horizontal angle than that of the vertical angle. Both the peak values of the occipital fossa pressure in effect simulations of the horizontal and vertical angles were three to four times of the peak values of the forehead pressure. When the impact horizontal angle was up to 255[Formula: see text], or the vertical angle was up to 45[Formula: see text], the head HIC(d) values would be up to 1320.45 and 1101.06, respectively, which indicated a AIS 3[Formula: see text] injury risk of the head.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

scholarly journals Comparison of Numerical Analyses with a Static Load Test of a Continuous Flight Auger Pile

2014 ◽  
Vol 22 (4) ◽  
pp. 1-10 ◽  
Author(s):  
Michal Hoľko ◽  
Jakub Stacho
Keyword(s):  
Load Distribution ◽  
Static Load ◽  
Numerical Models ◽  
Load Test ◽  
Numerical Analyses ◽  
Static Load Test ◽  
Material Models ◽  
The Common ◽  
Constitutive Material ◽  
The Impact

Abstract The article deals with numerical analyses of a Continuous Flight Auger (CFA) pile. The analyses include a comparison of calculated and measured load-settlement curves as well as a comparison of the load distribution over a pile's length. The numerical analyses were executed using two types of software, i.e., Ansys and Plaxis, which are based on FEM calculations. Both types of software are different from each other in the way they create numerical models, model the interface between the pile and soil, and use constitutive material models. The analyses have been prepared in the form of a parametric study, where the method of modelling the interface and the material models of the soil are compared and analysed. Our analyses show that both types of software permit the modelling of pile foundations. The Plaxis software uses advanced material models as well as the modelling of the impact of groundwater or overconsolidation. The load-settlement curve calculated using Plaxis is equal to the results of a static load test with a more than 95 % degree of accuracy. In comparison, the load-settlement curve calculated using Ansys allows for the obtaining of only an approximate estimate, but the software allows for the common modelling of large structure systems together with a foundation system.

Finite Element Analysis of Brain Injury due to Head Impact

2003 ◽  
Vol 17 (08n09) ◽  
pp. 1355-1361
Author(s):  
Chang Min Suh ◽  
Sung Ho Kim ◽  
Werner Goldsmith
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Brain Injury ◽  
Head Impact ◽  
Stress And Strain ◽  
Resonance Imaging ◽  
Element Analysis ◽  
External Impact ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Traumatic Brain Injury (TBI) due to head impact by external impactor was analyzed using Finite Element Method (FEM). Two-dimensiona modeling was performed according to Magnetic Resonance Imaging (MRI) data of Mongolian subject. Pressure variation in a cranium due to external impact was analyzed in order to simulate Nahum et al.'s cadaver test.6 And, analyzed results were compared with Nahum et al.'s experimental data.6 As results, stress and strain behaviors of the brain during impact were accorded with experimental data qualitatively even though there were some differences in quantitative values. In addition, they were accorded with other references about brain injury as well.

Techniques in Finite Element Modeling of Helmeted-Head Biomechanics

2009 ◽  
Author(s):  
Andrzej Przekwas ◽  
X. G. Tan ◽  
Z. J. Chen ◽  
Xianlian Zhou ◽  
Debbie Reeves ◽  
...  
Keyword(s):  
Finite Element ◽  
Brain Injury ◽  
Injury Risk ◽  
High Energy ◽  
Dense Layer ◽  
Hard Material ◽  
Absorption Mechanism ◽  
Military Applications ◽  
Main Components ◽  
The Impact

Generally a helmet comprises two main components: the shell and the fitting system. Despite the variations in designs due to the different usage requirements, typically helmets are intended to protect the user’s head through an energy absorption mechanism. The weight and volume are important factors in helmet design since both may alter the injury risk to the head and neck. The helmet outer shell is usually made of hard material that will deform when it is hit by hard objects. This action disperses energy from the impact to lessen the force before it reaches the head. The fitting system frequently includes a dense layer that cushions and absorbs the energy as a result of relative motion between the helmet and the head. A balance needs to be achieved on how strong and how stiff a helmet should be to provide the best possible protection. If a helmet is too stiff it can be less able to prevent brain injury in the kinds of impacts that may occur. If it is too flexible or soft, it might not protect the user in a violent, high-energy crash. For military applications, the requirements for helmet performance may be even more demanding. Not only do helmets have to protect a Soldier’s head from blunt impacts, but helmets also are expected to provide mounting platforms for ancillary devices and to function in ballistic and blast events as well.

Microstructural Hyperelastic Characterization of Brain White Matter in Tension

2019 ◽  
Author(s):  
Mohammadreza Ramzanpour ◽  
Mohammad Hosseini-Farid ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Human Brain ◽  
Volume Fraction ◽  
Fibrous Composite ◽  
Diffuse Axonal Injury ◽  
Stiffness Ratio ◽  
Fibrous Composite Material ◽  
Brain White Matter ◽  
The Brain

Abstract Axons as microstructural constituent elements of brain white matter are highly oriented in extracellular matrix (ECM) in one direction. Therefore, it is possible to model the human brain white matter as a unidirectional fibrous composite material. A micromechanical finite element model of the brain white matter is developed to indirectly measure the brain white matter constituents’ properties including axon and ECM under tensile loading. Experimental tension test on corona radiata conducted by Budday et al. 2017 [1] is used in this study and one-term Ogden hyperelastic constitutive model is applied to characterize its behavior. By the application of genetic algorithm (GA) as a black box optimization method, the Ogden hyperelastic parameters of axon and ECM minimizing the error between numerical finite element simulation and experimental results are measured. Inverse analysis is conducted on the resultant optimized parameters shows high correlation of coefficient (>99%) between the numerical and experimental data which verifies the accuracy of the optimization procedure. The volume fraction of axons in porcine brain white matter is taken to be 52.7% and the stiffness ratio of axon to ECM is perceived to be 3.0. As these values are not accurately known for human brain white matter, we study the material properties of axon and ECM for different stiffness ratio and axon volume fraction values. The results of this study helps to better understand the micromechanical structure of the brain and micro-level injuries such as diffuse axonal injury.

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