Development and quantification of an atlas-based method for model-updated image-guided neurosurgery

Abstract

This dissertation covers research regarding the use of computational models during brain tumor resection therapies. Systematic studies have shown that the brain tissue shifts during tumor resection therapies and that current image-guided systems do not account for this shift. Compensating for intraoperative brain shift using computational models has been used with promising results. For computational models to be clinically useful in tumor resection guidance, these models should meet the real-time constraints of neurosurgery and they should also provide images that mirror their intraoperative counterparts. The primary goal behind this research involves developing one such computational framework. More specifically, this framework involves combining a computational model with a linear inverse model to predict intraoperative brain shift. The framework reported in this dissertation relies on relatively inexpensive small scale computer clusters and can compute image updates on a time scale that is compatible with the surgical removal of tumor. In-vivo validation shows that the framework presented in this dissertation increases the efficiency and accuracy of image-guided systems. Results obtained have also been presented as graphical images for qualitative assessment. In summary this research constitutes a significant step towards using computational models for neuronavigation.