Creation of Computerized 3D MRI-Integrated Atlases of the Human Basal Ganglia and Thalamus

Functional brain imaging and neurosurgery in subcortical areas often requires visualization of brain nuclei beyond the resolution of current magnetic resonance imaging (MRI) methods. We present techniques used to create: (1) a lower resolution 3D atlas, based on the Schaltenbrand and Wahren print atlas, which was integrated into a stereotactic neurosurgery planning and visualization platform (VIPER); and (2) a higher resolution 3D atlas derived from a single set of manually segmented histological slices containing nuclei of the basal ganglia, thalamus, basal forebrain, and medial temporal lobe. Both atlases were integrated to a canonical MRI (Colin27) from a young male participant by manually identifying homologous landmarks. The lower resolution atlas was then warped to fit the MRI based on the identified landmarks. A pseudo-MRI representation of the high-resolution atlas was created, and a non-linear transformation was calculated in order to match the atlas to the template MRI. The atlas can then be warped to match the anatomy of Parkinson's disease surgical candidates by using 3D automated non-linear deformation methods. By way of functional validation of the atlas, the location of the sensory thalamus was correlated with stereotactic intraoperative physiological data. The position of subthalamic electrode positions in patients with Parkinson's disease was also evaluated in the atlas-integrated MRI space. Finally, probabilistic maps of subthalamic stimulation electrodes were developed, in order to allow group analysis of the location of contacts associated with the best motor outcomes. We have therefore developed, and are continuing to validate, a high-resolution computerized MRI-integrated 3D histological atlas, which is useful in functional neurosurgery, and for functional and anatomical studies of the human basal ganglia, thalamus, and basal forebrain

Ultra-High Field Magnetic Resonance Imaging for Stereotactic Neurosurgery

Stereotactic neurosurgery is a subspecialty within neurosurgery concerned with accurate targeting of brain structures. Deep brain stimulation (DBS) is a specific type of stereotaxy in which electrodes are implanted in deep brain structures. It has proven therapeutic efficacy in Parkinson’s disease and Essential Tremor, but with an expanding number of indications under evaluation including Alzheimer’s disease, depression, epilepsy, and obesity, many more Canadians with chronic health conditions may benefit. Accurate surgical targeting is crucial with millimeter deviations resulting in unwanted side effects including muscle contractions, or worse, vessel injury. Lack of adequate visualization of surgical targets with conventional lower field strengths (1.5/3 Tesla) has meant that standard-of-care surgical treatment has relied on indirect targeting using standardized landmarks to find a correspondence with a histological ``template\u27\u27 of the brain. For this reason, these procedures routinely require awake testing and microelectrode recording, which increases operating room time, patient discomfort, and risk of complications. Advances in ultra-high field (\u3e= 7 Tesla or 7T) imaging have important potential implications for targeting structures enabling better visualization as a result of its increased (sub-millimeter) spatial resolution, tissue contrast, and signal-to-noise ratio. The work in this thesis explores ways in which ultra-high field magnetic resonance imaging can be integrated into the practice of stereotactic neurosurgery. In Chapter 2, an ultra-high field MRI template is integrated into the surgical workflow to assist with planning for deep brain stimulation surgery cases. Chapter 3 describes a novel anatomical fiducial placement protocol that is developed, validated, and used prospectively to quantify the limits of template-assisted surgical planning. In Chapter 4, geometric distortions at 7T that may impede the ability to perform accurate surgical targeting are characterized in participant data, and generally noted to be away from areas of interest for stereotactic targeting. Finally, Chapter 5 discusses a number of important stereotactic targets that are directly visualized and described for the first time in vivo, paving the way for patient-specific surgical planning using ultra-high field MRI

Deformable brain atlas validation of the location of subthalamic nucleus using T1-weighted MR images of patients operated on for Parkinson's

[EN] Parkinson¿s disease is a degenerative disease of the central nervous system. One of the most effective treatments is deep brain stimulation. This technique requires the localization of an objective structure: the subthalamic nucleus. Unfortunately this structure is difficult to locate. In this work the creation of a deformable brain atlas that enables the identification of the subthalamic nucleus in T1-weighted magnetic resonance imaging (MRI) in an automatic, precise and fast way is presented. The system has been validated using data from 10 patients (20 nucleus) operated on for Parkinson¿s. Our system offers better results using a Wendland function with an error of 1.8853 ± 0.9959 mm.Ortega Pérez, M.; Juan Lizandra, MC.; Alcañiz Raya, ML.; Gil Gómez, JA.; Monserrat Aranda, C. (2008). Deformable brain atlas validation of the location of subthalamic nucleus using T1-weighted MR images of patients operated on for Parkinson's. Computerized Medical Imaging and Graphics. 32(5):367-378. doi:10.1016/j.compmedimag.2008.02.003S36737832

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

Multi-Material Mesh Representation of Anatomical Structures for Deep Brain Stimulation Planning

The Dual Contouring algorithm (DC) is a grid-based process used to generate surface meshes from volumetric data. However, DC is unable to guarantee 2-manifold and watertight meshes due to the fact that it produces only one vertex for each grid cube. We present a modified Dual Contouring algorithm that is capable of overcoming this limitation. The proposed method decomposes an ambiguous grid cube into a set of tetrahedral cells and uses novel polygon generation rules that produce 2-manifold and watertight surface meshes with good-quality triangles. These meshes, being watertight and 2-manifold, are geometrically correct, and therefore can be used to initialize tetrahedral meshes. The 2-manifold DC method has been extended into the multi-material domain. Due to its multi-material nature, multi-material surface meshes will contain non-manifold elements along material interfaces or shared boundaries. The proposed multi-material DC algorithm can (1) generate multi-material surface meshes where each material sub-mesh is a 2-manifold and watertight mesh, (2) preserve the non-manifold elements along the material interfaces, and (3) ensure that the material interface or shared boundary between materials is consistent. The proposed method is used to generate multi-material surface meshes of deep brain anatomical structures from a digital atlas of the basal ganglia and thalamus. Although deep brain anatomical structures can be labeled as functionally separate, they are in fact continuous tracts of soft tissue in close proximity to each other. The multi-material meshes generated by the proposed DC algorithm can accurately represent the closely-packed deep brain structures as a single mesh consisting of multiple material sub-meshes. Each sub-mesh represents a distinct functional structure of the brain. Printed and/or digital atlases are important tools for medical research and surgical intervention. While these atlases can provide guidance in identifying anatomical structures, they do not take into account the wide variations in the shape and size of anatomical structures that occur from patient to patient. Accurate, patient-specific representations are especially important for surgical interventions like deep brain stimulation, where even small inaccuracies can result in dangerous complications. The last part of this research effort extends the discrete deformable 2-simplex mesh into the multi-material domain where geometry-based internal forces and image-based external forces are used in the deformation process. This multi-material deformable framework is used to segment anatomical structures of the deep brain region from Magnetic Resonance (MR) data