On the Effects of Model Complexity in Computing Brain Deformation for Image-Guided Neurosurgery

Intra-operative images acquired during brain surgery do not provide sufficient detail to confidently locate brain internal structures that have been identified in high-resolution pre-operative images. However, the pre-operative images can be warped to the intra-operative position of brain using predicted deformation field. While craniotomy-induced brain shift deformation can be accurately computed using patient-specific finite element models in real-time, accurate segmentation and meshing of brain internal structures remains a time-consuming task. In this chapter, we conduct a parametric study to evaluate the sensitivity of the predicted brain shift deformation to model complexity, which includes the effects of disregarding the differences in properties between the parenchyma, tumour and ventricles and applying different approaches for representing the ventricles (as a very soft solid or cavity) to minimise segmentation and meshing effort for model generation. The results suggest that the difference in brain shift deformation predicted by models due to such variation is not significant. Segmentation of brain parenchyma and skull seems sufficient to build models that can accurately predict craniotomy-induced brain shift deformation.