Material characterization and simulation for soft gels subjected to impulsive loading.

For impact and blast experiments of traumatic brain injury (TBI), soft gel materials are used as surrogates to imitate the mechanical responses of brain tissue. To properly model a viscoelastic gel brain in a surrogate head using a finite element (FE) model, material parameters such as the shear moduli and relaxation time at high strain rates are required. However, such information is scarce in the literature and its applicability for a range of dynamic conditions is unclear. We used an integrated experiment and simulation approach to efficiently determine mechanical properties of soft gels at finite strains, as well as over a wide range of strain rates. A novel impact experiment using a gel block was developed to capture the high strain rate behavior by maximizing the inherent shear wave motion at different impact conditions. A corresponding computational model was used to simulate the gel dynamics of the impact. Parametric simulations utilizing optimization and correlation analyses were used to calibrate multiple material parameters in the nonlinear viscoelastic model to the experimental data. The optimal parameters for gels, including Sylgards 184, 3-6636, and 527, were found. We ascertained the initial shear stiffening effect in gels at high strain rate loadings experimentally and incorporated this effect in the simulation. We have verified the integrated approach by comparing the material properties of the gels with analytical results based on shear wave propagation. This study provides a new approach to calibrate the material behavior of soft gels under high strain rate loading conditions.

A Methodology to Compare Biomechanical Simulations With Clinical Brain Imaging Analysis Utilizing Two Blunt Impact Cases.

According to the US Defense and Veterans Brain Injury Center (DVBIC) and Centers for Disease Control and Prevention (CDC), mild traumatic brain injury (mTBI) is a common form of head injury. Medical imaging data provides clinical insight into tissue damage/injury and injury severity, and helps medical diagnosis. Computational modeling and simulation can predict the biomechanical characteristics of such injury, and are useful for development of protective equipment. Integration of techniques from computational biomechanics with medical data assessment modalities (e.g., magnetic resonance imaging or MRI) has not yet been used to predict injury, support early medical diagnosis, or assess effectiveness of personal protective equipment. This paper presents a methodology to map computational simulations with clinical data for interpreting blunt impact TBI utilizing two clinically different head injury case studies. MRI modalities, such as T1, T2, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), were used for simulation comparisons. The two clinical cases have been reconstructed using finite element analysis to predict head biomechanics based on medical reports documented by a clinician. The findings are mapped to simulation results using image-based clinical analyses of head impact injuries, and modalities that could capture simulation results have been identified. In case 1, the MRI results showed lesions in the brain with skull indentation, while case 2 had lesions in both coup and contrecoup sides with no skull deformation. Simulation data analyses show that different biomechanical measures and thresholds are needed to explain different blunt impact injury modalities; specifically, strain rate threshold corresponds well with brain injury with skull indentation, while minimum pressure threshold corresponds well with coup-contrecoup injury; and DWI has been found to be the most appropriate modality for MRI data interpretation. As the findings from these two cases are substantiated with additional clinical studies, this methodology can be broadly applied as a tool to support injury assessment in head trauma events and to improve countermeasures (e.g., diagnostics and protective equipment design) to mitigate these injuries.

Optimizing Helmet Pad Placement Using Computational Predicted Injury Pattern to Reduce Mild Traumatic Brain Injury.

INTRODUCTION: This effort, motivated and guided by prior simulated injury results of the unprotected head, is to assess and compare helmet pad configurations on the head for the effective mitigation of blast pressure transmission in the brain in multiple blast exposure environments. MATERIALS AND METHODS: A finite element model of blast loading on the head with six different helmet pad configurations was used to generate brain model biomechanical responses. The blast pressure attenuation performance of each pad configuration was evaluated by using the calculated pressure exposure fraction in the brain model. Monte Carlo simulations generated repetitive blast cumulative exposures. RESULTS: Significant improvement of a 6-Pad Modified configuration compared to a 6-Pad Baseline configuration indicates the importance of providing protection against the side blast. Both 12-Pad configurations are very effective in mitigating pressure in the brain. Repetitive blast exposure statistics for operational exposures shows that pad configurations with a larger number of pads and smaller gaps between pads perform better than the configurations with a smaller number of pads and larger gaps between pads. CONCLUSIONS: Optimizing helmet pad size and/or placement could provide an improved protection by minimizing the side blast orientation effects and mitigating high-pressure fields in the brain from repeated blast exposures.

Simulation of Cumulative Exposure Statistics for Blast Pressure Transmission Into the Brain.

INTRODUCTION: This study develops and demonstrates an analysis approach to understand the statistics of cumulative pressure exposure of the brain to repetitive blasts events. MATERIALS AND METHODS: A finite element model of blast loading on the head was used for brain model biomechanical responses. The cumulative pressure exposure fraction (CPEF), ranging from 0.0 to 1.0, was used to characterize the extent and repetition of high pressures. Monte Carlo simulations were performed to generate repetitive blast cumulative exposures. RESULTS: The blast orientation effect is as influential as the blast overpressure magnitudes. A 75° (from the side) blast orientation can produce CPEF values exceeding traumatic brain injury pressure thresholds >0.95 while, for the same blast overpressure, a 0° (front) blast orientation results in a CPEF <0.25. Monte Carlo results for different sequences reflecting notional operational and training environments show that both mean values and standard deviations of CPEF reach the statistically equilibrium state at a finite value of n exposures for each sequence. CONCLUSIONS: Statistical convergence of the brain pressure response metrics versus number of blasts for different exposures characterizes the transitions from "low" to "high" number of blasts and quantitatively highlights the differences between operational and training exposures.

Biomechanics of Blast TBI With Time-Resolved Consecutive Primary, Secondary, and Tertiary Loads.

Blast-induced traumatic brain injury (bTBI) has become a signature casualty of recent military operations. In spite of significant clinical and preclinical TBI research, current understanding of injury mechanisms and short- and long-term outcomes is limited. Mathematical models of bTBI biomechanics may help in better understanding of injury mechanisms and in the development of improved neuroprotective strategies. Until present, bTBI has been analyzed as a single event of a blast pressure wave propagating through the brain. In many bTBI events, the loads on the body and the head are spatially and temporarily distributed, involving the primary intracranial pressure wave, followed by the head rotation and then by head impact on the ground. In such cases, the brain microstructures may experience time/space distributed (consecutive) damage and recovery events. The paper presents a novel multiscale simulation framework that couples the body/brain scale biomechanics with micro-scale mechanobiology to study the effects of micro-damage to neuro-axonal structures. Our results show that the micro-mechanical responses of neuro-axonal structures occur sequentially in time with "damage" and "relaxation" periods in different parts of the brain. A new integrated computational framework is described coupling the brain-scale biomechanics with micro-mechanical damage to axonal and synaptic structures.

On the Development of Interspecies Traumatic Brain Injury Correspondence Rules.

Traumatic brain injury analysis in human is exceedingly difficult due to the methods in which data can be collected, thus many researchers commonly implement animal surrogates. However, use of these surrogates is costly and restricted by ethical concerns and test logistics. Computational models and simulations do not have these constraints and can produce significant amounts of data in relatively short periods. This paper shows the development of a human head and neck model and a full body porcine model. Both models are developed from high-resolution CT and MRI scans and the latest low-to-high strain rate mechanical data available in the literature to represent tissue component material behaviors. Both models are validated against experiments from the literature and used to complete an initial interspecies correspondence rule development study for blast overpressure effects. The results indicate the similarities in the way injury develops in the pig brain and human brain but these similarities occur at very different insult levels. These results are extended by a study, which shows that blast peak pressure is the driving factor in injury prediction and, depending on the injury metric used, significantly different injuries could be predicted.

Towards Identification of Correspondence Rules to Relate Traumatic Brain Injury in Different Species.

Traumatic brain injury analysis in humans is exceedingly difficult due to the intrusive methods by which data can be collected; thus, many researchers commonly implement animal surrogates. However, ethical concerns and cost limit the scope of these tests on animal subjects too. Computational models, which provide an alternative method to data collection, are not constrained by these concerns and are able to generate significant amounts of data in relatively short time. This paper shows how the data generated from models of a human and pig head can be used towards developing interspecies correspondence rules for blast overpressure effects. The blast overpressure is simulated using an explosive of known weight and standoff distance and injury is evaluated using criteria in published literature. Results indicate that equivalent blasts in the human and pig produce significantly different injuries, and when equating total injured brain volume, the locations of injury in the brain vary between the species. Charge weight and total injured brain volume are related using a linear regression of the data such that a known injury in the pig or known blast can be used to predict injury or the blast experienced by a human, thus creating a correspondence between the species.

Effect of human head morphological variability on the mechanical response of blast overpressure loading.

A methodology is introduced to investigate the effect of intersubject head morphological variability on the mechanical response of the brain when subjected to blast overpressure loading. Nonrigid image registration techniques are leveraged to warp a manually segmented template model to an arbitrary number of subjects following a procedure to coarsely segment the subjects in batch. Finite element meshes are autogenerated, and blast analysis is conducted. The template model is initially constructed to enable the full automated implementation and application of the proposed methodology. The application of the proposed approach for an anterior-oriented blast has been demonstrated, and the results reveal that the pressure response in the brain does exhibit some dependence on head morphological variability. While the magnitude of the peak pressure response can vary by more than 30%, its location within the brain is unaffected by head morphological variability. A linear least squares analysis was conducted to demonstrate that the peak magnitude of pressure is uncorrelated with head volume while it is correlated with aspect ratio relating to the amount of exposed surface area to the blast. These features of the pressure response are likely due to the peak pressure occurring during the early stages of stress wave transmission and reflection. As a result, the pressure response due to blast overpressure loading is predominantly loading dependent while morphological variability has a secondary effect.