The development of an in-silico model of the human head for the simulation of traumatic brain injury. (2021)
Type of ContentTheses / Dissertations
Thesis DisciplineMechanical Engineering
Degree NameMaster of Engineering
PublisherUniversity of Canterbury
The anatomy of the human head is highly complex, consisting of several different layers of biological material. The layers in the brain are both solid and liquid. The tissues and fluids which are present in the head have been shown to exhibit non-linear material parameters including hyper elasticity, viscoelasticity and anisotropy. As result modelling of the human head is a complex challenge.
Traumatic brain injury (TBI) classification methods are somewhat arbitrary and stochastic in nature. Currently, the accuracy of head injury diagnosis is often dependent on the past experiences and knowledge of the individual practitioner. For this reason, the development of a human head model is beneficial. Due to the inherent complexity of human anatomy, an accurate representation of head geometry is computationally expensive.
A proposed head model was developed in ANSYS and consists of a skull, the cerebrospinal fluid (CSF) and the brain. The final geometry of the model was established via conversion of magnetic resonance imaging (MRI) and computed tomography (CT) scans into .stl files. The brain was represented as a 12 term Ogden Hyperelastic solid with a 12 term Prony series approximation of viscoelastic damping. The skull was represented as a 3 term Prony viscoelastic damped solid. The CSF was represented as a fluid-like solid material with a large Youngs modulus and small shear modulus. Simulation was run using ANSYS transient mechanical solver.
Simulated results showed similar patterns of stress distribution, implying that the CSF has a major effect on the way stress is propagated throughout the brain. This was hypothesized to be a function of both the material parameters of the CSF and the geometry of the head. The final model was able to converge to a 10s simulated impact after approximately 2 hours of computational time. The model developed for this thesis enables further insights into the inner mechanics of TBI and will benefit from further development.
A secondary analysis considered the effects of soft tissue artefact on measurements collected by skin mounted acceleration sensors. The skin, skull, sensor system was modelled as a non-linear spring mass damper system. Comparison of simulated results to acceleration results collect via mouthguard mounted sensors showed accurate representation of the skin, outlining the need to include the dynamic response of the skin when performing analysis on results collected from skin mounted sensors.
RightsAll Right Reserved
Showing items related by title, author, creator and subject.
3D kernel-density stochastic model for more personalized glycaemic control: Development and in-silico validation Davidson S; Desaive T; Benyo B; Shaw, Geoff; Knopp, Jennifer; Chase, Geoff; Uyttendaele, Vincent (Springer Science and Business Media LLC, 2019)Background: The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- A nd intra-patient metabolic variability results in increased ...
Coullie, Charis Blythe (University of Canterbury. Psychology, 2013)Introduction: Mild traumatic brain injury (mTBI) accounts for the vast majority of all paediatric TBI cases. It is an important public health concern, yet the long-term psychiatric and behavioural outcomes remain imperfectly ...
Predictive inference comprehension in adults with traumatic brain injury (TBI): The effects of salience and working memory Todd, Tamaryn Dee (University of Canterbury. Department of Communication Disorders, 2011)Objective: The purpose of this study was to investigate the impact of salience on the comprehension of predictive inferences in adults with traumatic brain injury (TBI), by increasing the visual salience of the predictive ...