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.
|Qualification||Doctor of Philosophy|
|Award date||11 Jul 2022|
|Publication status||Unpublished - 2022|