Diffuse axonal injury (DAI) is a common pathology in traumatic brain injury (TBI), which occurs when sudden accelerations to the head cause excessive stretching and shearing of axonal fibers, eventually leading to neuropathologic damage and dysfunction. To accurately capture the full-field kinematics of brain and its substructures when the head is subjected to rapid rotational accelerations about the vertical axis (in the axial plane), a fully subject-specific computational human head model is developed in this study. Under these loading conditions, it is experimentally observed using the tagged magnetic resonance imaging (tMRI) technique that a particular axial layer of brain passing through the central region of the corpus callosum exhibits maximum vulnerability both in terms of the area with axonal strain above a threshold value and the maximum axonal strain (MAS). Furthermore, it is noted that the out-of-plane deformation of this axial layer is very small compared to the in-plane deformations. Based on these observations, only this two-dimensional (2D) brain layer is considered in the model development.The geometry of the 2D head model is based on the brain anatomy and morphology information obtained directly via magnetic resonance imaging (MRI) at a resolution of 1.5 mm x 1.5 mm x 1.5 mm. Further, the viscoelastic material parameters of brain tissue are derived from in-vivo magnetic resonance elastography (MRE) at three actuation frequencies (30, 50 and 70 Hz). Lastly, the brain kinematics data under mild rotational accelerations to the skull, which is used both for the identification of the most vulnerable axial brain layer and for model validation, is extracted from tMRI technique. All the three experiments (i.e., MRI, MRE and tMRI) are conducted on the same human subject (a 31-year-old male), leading to a fully subject-specific model. Notably, unlike the currently available computational models that employ linear viscoelastic (LVE) or quasi-linear visco-hyperelastic constitutive models (e.g., the Holzapfel-Ogden-Gasser (HOG) model) and thus exhibit poor accuracy beyond the small strain regime (~0.1%), the computational model developed in this work employs a recently proposed viscous dissipation-based finite strain visco-hyperelastic constitutive model, which can effectively capture the nonlinear strain stiffening and strain rate sensitivity responses that are characteristic of brain tissue under injury-relevant loading conditions. To derive the visco-hyperelastic material parameters, a hybrid in vivo-ex vivo parameterization method is developed, in which an initial optimal parameter set is first calibrated from the linear viscoelastic material response obtained from MRE, which is then improved via a nonlinear constraint optimization to fit the large strain viscous overstress from ex vivo mechanical testing data in the literature. The resulting 2D head model simulations (run in the Uintah software via the meshless material point method) are validated against tMRI data in terms of the spatial and temporal accuracy of strain predictions. Specifically, the spatial accuracy of prediction of MAS, maximum principal strain (MPS) and the three in-plane Green-Lagrange strain components is quantified in terms of the Sørensen-Dice coefficient (SDC), and corresponding temporal accuracy is quantified in terms of the weighted average CORrelation and Analysis (CORA) scores.The 2D human head model developed in this study shows a good overall agreement with the tMRI data. For both the Green-Lagrange strain components and the MPS, the CORA score is greater than 0.60, which is an improvement over the existing computational head models in the literature. Notably, an excellent spatial accuracy of MAS prediction is observed, with an average SDC of 0.70, which highlights the potential utility of this model in identifying DAI locations. Finally, the simulation results of three constitutive models: LVE model, HGO model, and the finite strain visco-hyperelastic model are compared. It is shown that the finite strain visco-hyperelastic framework utilized in this work significantly improves the accuracy of model predictions. For example, the MAS SDC of the present model is 0.70, whereas in the case of LVE and HGO models, it is 0.52 and 0.53, respectively; a similar improvement in terms of CORA scores is also observed.

A Two-Dimensional Subject-Specific Human Head Model Based on the Viscous Dissipation-Based Visco-Hyperelastic Constitutive Framework

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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