Biomechanics and modeling in mechanobiology
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Biomech Model Mechanobiol · Apr 2021
An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain.
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 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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Bridging veins (BVs) drain the blood from the cerebral cortex into dural sinuses. BVs have one end attached to the brain and the other to the superior sagittal sinus (SSS), which is attached to the skull. Relative movement between these two structures can cause BV to rupture producing acute subdural haematoma, a head injury with a mortality rate between 30 and 90%. ⋯ The less common (4[Formula: see text]12 samples) had 2 layers and 7 to 34 times thicker collagen bundles on the outer layer. Fibre angle analysis showed that collagen was oriented mainly along the axial direction of the vessel. The von Mises fittings showed that in order to describe the fibre distribution 3 components were needed with mean angles [Formula: see text] at [Formula: see text] 0.35, 0.21, [Formula: see text] 0.02 rad or [Formula: see text] 20.2[Formula: see text], 12.1[Formula: see text], [Formula: see text] 1.2[Formula: see text] relative to the vessel's axial direction which was also the horizontal scan direction.
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Biomech Model Mechanobiol · Jun 2020
Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury.
With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. ⋯ The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.
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Biomech Model Mechanobiol · Dec 2019
Determining constitutive behavior of the brain tissue using digital image correlation and finite element modeling.
Detailed knowledge about the mechanical properties of brain can improve numerical modeling of the brain under various loading conditions. The success of this modeling depends on constitutive model and reliable extraction of its material constants. The isotropy of the brain tissue is a key factor which affects the form of constitutive models. ⋯ Then, the significance of using a correct method to extract the material constants of brain was discussed. In other words, the effect of the real boundary conditions in experiments, which was neglected in most previous studies, was taken into account here. Finally, the particle swarm optimization algorithm along with the finite element modeling was used to estimate the hyper-viscoelastic constants of different parts of the brain tissue.
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Biomech Model Mechanobiol · Oct 2019
Paediatric brain tissue properties measured with magnetic resonance elastography.
The aim of this study is to characterise the stiffness of white and grey matter in paediatric subjects using magnetic resonance elastography (MRE) and to determine whether these properties change throughout normal development. MRE was performed using a clinical 3T MRI scanner at three frequencies (30, 40 and 60 Hz) on 36 healthy paediatric subjects aged between 7 and 18 years (19 F) and 11 adults aged 23-44 years (6 F). Anatomical and diffusion tensor imaging was also collected. ⋯ Adult G*, FA, MD and volume values were within range of others reported in the literature. Paediatric white and grey matter stiffness values are similar to those of adults. We conclude that clinically, adult values can be used as a baseline measure in paediatric brain MRE.