Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work

Automatic finite elements mesh generation from planar contours of the brain: an image driven 'blobby' approach

In this paper, we address the problem of automatic mesh generation for finite elements modeling of anatomical organs for which a volumetric data set is available. In the first step a set of characteristic outlines of the organ is defined manually or automatically within the volume. The outlines define the "key frames" that will guide the procedure of surface reconstruction. Then, based on this information, and along with organ surface curvature information extracted from the volume data, a 3D scalar field is generated. This field allows a 3D reconstruction of the organ: as an iso-surface model, using a marching cubes algorithm; or as a 3D mesh, using a grid "immersion" technique, the field value being used as the outside/inside test. The final reconstruction respects the various topological changes that occur within the organ, such as holes and branching elements

Computational ultrasound tissue characterisation for brain tumour resection

In brain tumour resection, it is vital to know where critical neurovascular structuresand tumours are located to minimise surgical injuries and cancer recurrence. Theaim of this thesis was to improve intraoperative guidance during brain tumourresection by integrating both ultrasound standard imaging and elastography in thesurgical workflow. Brain tumour resection requires surgeons to identify the tumourboundaries to preserve healthy brain tissue and prevent cancer recurrence. Thisthesis proposes to use ultrasound elastography in combination with conventionalultrasound B-mode imaging to better characterise tumour tissue during surgery.Ultrasound elastography comprises a set of techniques that measure tissue stiffness,which is a known biomarker of brain tumours. The objectives of the researchreported in this thesis are to implement novel learning-based methods for ultrasoundelastography and to integrate them in an image-guided intervention framework.Accurate and real-time intraoperative estimation of tissue elasticity can guide towardsbetter delineation of brain tumours and improve the outcome of neurosurgery. We firstinvestigated current challenges in quasi-static elastography, which evaluates tissuedeformation (strain) by estimating the displacement between successive ultrasoundframes, acquired before and after applying manual compression. Recent approachesin ultrasound elastography have demonstrated that convolutional neural networkscan capture ultrasound high-frequency content and produce accurate strain estimates.We proposed a new unsupervised deep learning method for strain prediction, wherethe training of the network is driven by a regularised cost function, composed of asimilarity metric and a regularisation term that preserves displacement continuityby directly optimising the strain smoothness. We further improved the accuracy of our method by proposing a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. We then demonstrateinitial results towards extending our ultrasound displacement estimation method toshear wave elastography, which provides a quantitative estimation of tissue stiffness.Furthermore, this thesis describes the development of an open-source image-guidedintervention platform, specifically designed to combine intra-operative ultrasoundimaging with a neuronavigation system and perform real-time ultrasound tissuecharacterisation. The integration was conducted using commercial hardware andvalidated on an anatomical phantom. Finally, preliminary results on the feasibilityand safety of the use of a novel intraoperative ultrasound probe designed for pituitarysurgery are presented. Prior to the clinical assessment of our image-guided platform,the ability of the ultrasound probe to be used alongside standard surgical equipmentwas demonstrated in 5 pituitary cases

A numerical framework for interstitial fluid pressure imaging in poroelastic MRE

A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed

Towards efficient neurosurgery: Image analysis for interventional MRI

Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions

Enhancing Registration for Image-Guided Neurosurgery

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