Abstract
This thesis describes a biomechanics-based image registration method that compensates for the tumour resection-induced brain shift during neurosurgery. Initially, I used the finite element method (FEM) to compute the resection-induced brain deformation. However, using FEM to compute soft tissue deformations comes with a number of limitations, such as volumetric locking and time-consuming mesh generation. To circumvent these limitations, I used the Meshless Total Lagrangian Explicit Dynamics algorithm to compute the resection-induced brain deformation. Finally, I demonstrated how the modelling process can be automated to facilitate image-guided neurosurgery. To validate the methodology, I compared the results with the retrospective clinical data.
Original language | English |
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Qualification | Doctor of Philosophy |
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Award date | 11 Jul 2022 |
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Publication status | Unpublished - 2022 |