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Development and Validation of an MRI-Derived Head-Neck Finite Element Model

Bahreinizad, H.; Chowdhury, S. K.; Wei, L.; Paulon, G.; Santos, F.

2023-02-13 bioengineering
10.1101/2023.02.12.528203 bioRxiv
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PurposeThis study aimed to develop and validate a magnetic resonance imaging (MRI)-based biofidelic head-neck finite element (FE) model comprised of scalp, skull, CSF, brain, dura mater, pia mater, cervical vertebrae, and discs, 14 ligaments, and 42 neck muscles. MethodsWe developed this model using head and neck MRI images of a healthy male participant and by implementing a novel meshing algorithm to create finer hexahedral mesh structures of the brain. The model was validated by replicating four experimental studies: NBDLs high acceleration profile, Itos frontal impact cervical vertebrae study, Alshareefs brain sonomicrometry study, and Nahums impact study. ResultsThe results showed reasonable geometrical fidelity. Our simulated brain displacement and cervical disc strain results were close to their experimental counterparts. The intracranial pressure and brain stress data of our head-only model (excluding neck structures and constraining the base of the skull) were similar to Nahums reported results. As neck structures were not considered in Nahums study, the FE results of our head-neck model showed slight discrepancies. Notably, the addition of neck structures (head-neck model) reduced brain stress values and uncovered the brains intracranial pressure dynamics, which the head-only model failed to capture. Nevertheless, the FE simulation results showed a good agreement (r > 0.97) between the kinematic responses of the head-neck model and NBDLs experimental results. ConclusionThe developed head-neck model can accurately replicate the experimental results and has the potential to be used as an efficient computational tool for brain and head injury biomechanics research. Statements and DeclarationsThis work was primarily supported by the U.S. Department of Homeland Security (70RSAT21CB0000023). The MRI data acquisition was supported by the Texas Tech Neuroimaging Center.

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