Modelling the Presence of Diffuse Axonal Injury in Primary Phase Blast-Induced Traumatic Brain Injury

  • Matthew Sinclair
  • Adam Wittek
  • Barry Doyle
  • Karol Miller
  • Grand R. Joldes
Conference paper

Abstract

Blast-induced traumatic brain injury (TBI) has been an affliction of war since the advent of militarised explosives and has become even more prominent with the resurgence of improvised explosive devices (IEDs). A common injury resulting from these blast events is diffuse axonal injury (DAI), a clandestine type of TBI often occurring with no external visible symptoms. A voxel-based finite element model of the human head allows for simulation of trauma mechanisms derived from hemispherical surface blast scenarios experimentally determined to have a greater than 99 % survival rate by Bowen et~al. (Estimate of man’s tolerance to the direct effects of air blast, 1968). Coupling with in vivo results pertaining to DAI thresholds enabled introductory conclusions to be determined about the presence of DAI in survivable blast-trauma events. The blast events were simulated for the TNT mass equivalent of three different IEDs located at varying distances depending on the predicted survivability of the event. ABAQUS Explicit was used to conduct the finite element analysis and the Conventional Weapons (CONWEP) Blast Loading interface was used to calculate the hemispherical surface blast parameters. Areas of high strain occurred at the white/grey matter interface and brainstem for all simulations, as would be expected in a typical human head response. For the simulations in the lung damage classification, there was insufficient strain to predict the presence of DAI. Conversely, most of the simulations from the 99 % survivability distance produced sufficient strain to suggest DAI. Therefore, it was determined that blast events categorised as having a 99 % survivability demonstrate sufficient strain to suggest at least mild DAI.

Keywords

Traumatic brain injury Diffuse axonal injury Improvised explosive devices Voxel-based finite element model 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Matthew Sinclair
    • 1
  • Adam Wittek
    • 1
  • Barry Doyle
    • 1
  • Karol Miller
    • 1
  • Grand R. Joldes
    • 1
  1. 1.Intelligent Systems for Medicine LaboratoryThe University of Western AustraliaPerthAustralia

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