Mesh Convergence Behavior and the Effect of Element Integration of a Human Head Injury Model

Zhao, Wei; Ji, Songbai

doi:10.1007/s10439-018-02159-z

Cited by 30 publications

(17 citation statements)

References 42 publications

(83 reference statements)

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“…Earlier studies have shown models with finer mesh would lead to large brain strains when other modeling parameter are the same. 27 , 53 Similarly, the variations in mesh density among the eight brain models may contribute to the difference in strain predictions. However, it is difficult to isolate and quantify the effect that the differences in numerical approaches has on the correlations between models and their rankings, because these factors are often interactive.…”

Section: Discussionmentioning

confidence: 99%

Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models

et al. 2021

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Bicycle helmets are shown to offer protection against head injuries. Rating methods and test standards are used to evaluate different helmet designs and safety performance. Both strain-based injury criteria obtained from finite element brain injury models and metrics derived from global kinematic responses can be used to evaluate helmet safety performance. Little is known about how different injury models or injury metrics would rank and rate different helmets. The objective of this study was to determine how eight brain models and eight metrics based on global kinematics rank and rate a large number of bicycle helmets (n=17) subjected to oblique impacts. The results showed that the ranking and rating are influenced by the choice of model and metric. Kendall’s tau varied between 0.50 and 0.95 when the ranking was based on maximum principal strain from brain models. One specific helmet was rated as 2-star when using one brain model but as 4-star by another model. This could cause confusion for consumers rather than inform them of the relative safety performance of a helmet. Therefore, we suggest that the biomechanics community should create a norm or recommendation for future ranking and rating methods.

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Section: Discussionmentioning

confidence: 99%

Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models

et al. 2021

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“…Indeed, studies have demonstrated that models with finer meshes would lead to the prediction of larger brain–skull relative motions and larger brain strains with the same set of material properties (Giudice et al. 2019 ; Zhao and Ji 2019a , b ). It is thus suggested that it may not be appropriate to adopt material properties of the brain obtained from a coarse mesh to a model with a much finer mesh, and vice versa (Zhao and Ji 2019a , b ).…”

Section: Discussionmentioning

confidence: 99%

“…Continued efforts on model enhancement, including material model improvement, incorporating diffusion tensor imaging (DTI), brain–skull interface improvement, as well as mesh refinement, have led to updated versions compared to the original. Especially recent efforts on mesh refinement led to an average brain element size of about 1.8 mm in the WHIM V1.5 model (Zhao and Ji 2019a , b , 2020 ), while the element sizes in GHBMC (Mao et al 2013a ) and the refined THUMS (Atsumi et al. 2016 ) are also on the order of millimeter, being 2 mm, and 1.2 ~ 5 mm, respectively.…”

Section: Introductionmentioning

confidence: 99%

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Biomech Model Mechanobiol

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Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

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“…For parametric studies, the model response has been evaluated using Pearson's correlation coefficient (r), linear regression slope (m), and correlation score (CS). Details of these measures can be found in Ganpule et al (2017) and Zhao and Ji (2019) and avoided here for brevity. Differences are considered to be statistically significant if one of the following conditions has been met.…”

Section: Discussionmentioning

confidence: 99%

Biomechanical Analysis of Woodpecker Response During Pecking Using a Two-Dimensional Computational Model

Ganpule

Sutar

Shinde

2020

Front. Bioeng. Biotechnol.

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Traumatic brain injury (TBI) and chronic traumatic encephalopathy (CTE) due to the impact is a critical health concern. Impact mitigation strategy is a vital design paradigm to reduce the burden of TBI and CTE. In this regard, woodpecker biomimicry continues to attract attention. However, a direct comparison between a woodpecker and human biomechanical responses is lacking. Toward this end, we investigate the biomechanical response of a woodpecker during pecking using a two-dimensional head model. We also analyze the response of concurrent human head model to facilitate direct comparison with woodpecker response. The head models of woodpecker and human were built from medical images, the material properties were adopted from the literature. Both woodpecker and human head models were subjected to head kinematics obtained during pecking and resulting biomechanical response is studied. For the pecking cycle simulated in this work, peak rotational velocity and acceleration were ∼15 rad/s and 7,057 rad/s 2. These peak values are commensurate with the kinematics threshold values reported in human TBI. Our results show that, for the same input acceleration, the strains and stresses in the woodpecker brain are approximately six times lower than that of the human brain. The stress reduction is mainly attributed to the smaller size of the woodpecker head. The effect of pecking frequency and multiple pecking cycles have also been studied. It is observed that the strains and stresses in the brain are increased by ∼100% as pecking frequency is doubled. During multiple pecking cycle, dwell period of ∼90 ms tend to relax the stresses in the woodpecker brain; however, the amount of relaxation depends on the value of the decay constant. The comparison of biomechanical response against the axonal injury threshold suggests that for peak rotational acceleration of 7,057 rad/s 2 the maximum principal strain in the brains of woodpecker and human exceed the threshold limit. Acceleration scaling relationship between a woodpecker and equivalent human response is also developed as a function of head size. We obtain a scaling factor, a h a w , of 0.11 for baseline head sizes and a scaling factor of 1.03 as the human head size approaches woodpecker head size.

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Mesh Convergence Behavior and the Effect of Element Integration of a Human Head Injury Model

Cited by 30 publications

References 42 publications

Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models

Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Biomechanical Analysis of Woodpecker Response During Pecking Using a Two-Dimensional Computational Model

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