Abstract
According to the US Defense and Veterans Brain Injury Center (DVBIC) and Centers for Disease Control and Prevention (CDC), mild traumatic brain injury (mTBI) is a common form of head injury. Medical imaging data provides clinical insight into tissue damage/injury and injury severity, and helps medical diagnosis. Computational modeling and simulation can predict the biomechanical characteristics of such injury, and are useful for development of protective equipment. Integration of techniques from computational biomechanics with medical data assessment modalities (e.g., magnetic resonance imaging or MRI) has not yet been used to predict injury, support early medical diagnosis, or assess effectiveness of personal protective equipment. This paper presents a methodology to map computational simulations with clinical data for interpreting blunt impact TBI utilizing two clinically different head injury case studies. MRI modalities, such as T1, T2, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), were used for simulation comparisons. The two clinical cases have been reconstructed using finite element analysis to predict head biomechanics based on medical reports documented by a clinician. The findings are mapped to simulation results using image-based clinical analyses of head impact injuries, and modalities that could capture simulation results have been identified. In case 1, the MRI results showed lesions in the brain with skull indentation, while case 2 had lesions in both coup and contrecoup sides with no skull deformation. Simulation data analyses show that different biomechanical measures and thresholds are needed to explain different blunt impact injury modalities; specifically, strain rate threshold corresponds well with brain injury with skull indentation, while minimum pressure threshold corresponds well with coup–contrecoup injury; and DWI has been found to be the most appropriate modality for MRI data interpretation. As the findings from these two cases are substantiated with additional clinical studies, this methodology can be broadly applied as a tool to support injury assessment in head trauma events and to improve countermeasures (e.g., diagnostics and protective equipment design) to mitigate these injuries.
Highlights
Traumatic brain injury (TBI) has become a growing health concern worldwide, leading to a wide range of problems from mild memory deficits to persistent vegetative states (Florence et al, 2018)
The hospital report indicated that the individual was a pedestrian who was hit by a speeding motor vehicle from the front, sustained a concussion due to the backward fall on the ground, and suffered from loss of consciousness for a period of approximately 20 min
The magnetic resonance imaging (MRI) data revealed a depressed fracture of the right parietal bone of the skull with a hemorrhagic contusion in the underlying brain parenchyma and associated subdural hematoma in the first row of Figure 1
Summary
Traumatic brain injury (TBI) has become a growing health concern worldwide, leading to a wide range of problems from mild memory deficits to persistent vegetative states (Florence et al, 2018). Depending upon the extent of the insult and the intensity of the impact, injuries can result in combinations of skull fractures, brain hemorrhage, contusions, subdural hematoma, and diffusive axonal injuries, which can be diagnosed from mild to severe range depending on GCS scores (Post et al, 2015). Similar GCS scores can be assigned to vastly different brain injuries ranging from localized lesions with/without skull fractures to coup and contrecoup without any presentation of outward signs of injury. While moderate to severe injuries are frequently and diagnosed based upon impairments that are clinically obvious, mild injuries have been much harder to diagnose, and distinguishing the extent of the injury based upon clinical presentation with GCS scores is typically challenging
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