Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747471
Title: Enhancing registration for image-guided neurosurgery
Author: Kochan, Martin
ISNI:       0000 0004 7230 9226
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Pharmacologically refractive temporal lobe epilepsy and malignant glioma brain tumours are examples of pathologies that are clinically managed through neurosurgical intervention. The aims of neurosurgery are, where possible, to perform a resection of the surgical target while minimising morbidity to critical structures in the vicinity of the resected brain area. Image-guidance technology aims to assist this task by displaying a model of brain anatomy to the surgical team, which may include an overlay of surgical planning information derived from preoperative scanning such as the segmented resection target and nearby critical brain structures. Accurate neuronavigation is hindered by brain shift, the complex and non-rigid deformation of the brain that arises during surgery, which invalidates assumed rigid geometric correspondence between the neuronavigation model and the true shifted positions of relevant brain areas. Imaging using an interventional MRI (iMRI) scanner in a next-generation operating room can serve as a reference for intraoperative updates of the neuronavigation. An established clinical image processing workflow for iMRI-based guidance involves the correction of relevant imaging artefacts and the estimation of deformation due to brain shift based on non-rigid registration. The present thesis introduces two refinements aimed at enhancing the accuracy and reliability of iMRI-based guidance. A method is presented for the correction of magnetic susceptibility artefacts, which affect diffusion and functional MRI datasets, based on simulating magnetic field variation in the head from structural iMRI scans. Next, a method is presented for estimating brain shift using discrete non-rigid registration and a novel local similarity measure equipped with an edge-preserving property which is shown to improve the accuracy of the estimated deformation in the vicinity of the resected area for a number of cases of surgery performed for the management of temporal lobe epilepsy and glioma.
Supervisor: Stoyanov, D. ; Ourselin, S. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.747471  DOI: Not available
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