Non-linear finite element biomechanical model as a registration tool for image-guided neurosurgery

[Truncated abstract] Neurosurgical resection is the primary therapeutic intervention in the treatment of cerebral gliomas. Near-total surgical removal is difficult due to the uncertainty in visual distinction of gliomatous tissue from adjacent healthy brain tissue. More complete tumour removal can be achieved through image-guided neurosurgery that uses intraoperative MRIs for improved visualization. The efficiency of intra-operative visualization and monitoring can be significantly improved by fusing high resolution pre-operative imaging data with the intra-operative configuration of the patient’s brain. This can be achieved by updating the pre-operative image to the current intra-operative configuration of the brain through registration. However, brain shift occurs during craniotomy (due to several factors including the loss of cerebrospinal fluid (CSF), changing pressure balances due to the impact of physiological factors and the effect of anaesthetics, and mechanical effects such as the impact of gravity on the brain tissue, and resection of tissue, etc.) and hence should be accounted for while registering the images. The overall objective of this thesis is to significantly improve the efficacy and efficiency of image-guided neurosurgery for brain tumours by incorporating realistic computation of brain deformations, based on a fully non-linear biomechanical model, in a system to improve intra-operative visualisation, navigation and monitoring. The system will create an augmented reality visualisation of the intra-operative configuration of the patient’s brain merged with high resolution pre-operative imaging data, including functional magnetic resonance imaging and diffusion tensor imaging, in order to better localise the tumour and critical healthy tissues...