Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Motor and higher‐order functions topography of the human dentate nuclei identified with tractography and clustering methods

Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher-order functions in humans in vivo. Whole-brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion-derived metrics in an unsupervised fuzzy c-means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule-specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non-motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule-specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher-order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non-motor functions and that connectivity-based and microstructure-based atlases provide complementary information. These results represent a step-ahead for the interpretation of pathological conditions involving cerebro-cerebellar circuits

Development and evaluation of a novel framework for subcortical gray matter segmentation using quantitative magnetic susceptibility and R2* mapping

Quantitative susceptibility mapping (QSM) and effective relaxation rate (R∗2) mapping are promising magnetic resonance imaging (MRI) techniques to study iron content in the human brain in vivo. The ability to quantify iron content in subcortical gray matter (SGM) is important to better understand its role in neurodegenerative diseases as well as during normal brain aging. However, accurate determination of tissue magnetic susceptibility and R∗2 in brain structures, such as SGM, may be challenging due to potential segmentation inaccuracies, specifically when performed automatically. The present thesis introduces a robust framework to automatically segment and characterize SGM using quantitative susceptibility maps and exemplarily applies it to investigate iron-related susceptibility and R∗2 changes in patients with multiple sclerosis (MS) in comparison to controls

White Matter Degeneration in Huntington's Disease: A Study of Brain Structure and Cognition

Huntington's disease (HD) is a hereditary neurodegenerative disorder characterised by devastating physical, behavioural and mental dysfunction. Accumulating evidence indicates that abnormal white matter (WM) is a major hallmark of the disease, with both macro- and microstructural changes apparent before manifest diagnosis. This thesis is an investigation of WM in HD and uses various imaging and cognitive techniques to address some key challenges. Firstly, the development of reliable structural measurement techniques sensitive to longitudinal change may aid characterisation of subtle abnormalities before disease onset. Secondly, optimised diffusion imaging techniques which incorporate superior image processing tools will further understanding as to why changes are harder to find in the premanifest stage and will increase sensitivity to detect them. Thirdly, the development of novel, hypothesis-driven neuropsychological tasks will help detect heterogeneous cognitive decline in individuals in the earliest disease stages. To address these challenges, firstly, a novel corpus callosum (CC) segmentation technique is developed and applied to a large clinical cohort revealing disease-related reduction in baseline CC volume and elevated rates of change over 24 months in both premanifest and manifest HD participants. Secondly, an investigation of template effects in diffusion image analysis reveals consistency between analyses using three customised templates and evidence of the superiority of tensor-based registration over scalar-based registration is demonstrated. An exploratory investigation into the association between brain volume and WM diffusivity is also presented and disease-specific changes in HD gene-carriers are reported. Lastly, two specially designed, pathology-targeted cognitive tasks are applied to a premanifest HD cohort. Abnormal interhemispheric transfer from the non-dominant to dominant hemisphere as well as altered attentional processing and impaired automaticity is revealed. By developing techniques to characterise WM pathology and explore cognitive deficits, this thesis improves our understanding of the role of WM degeneration in the premanifest and early stages of HD

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging.

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field

Robust Algorithms for Registration of 3D Images of Human Brain

This thesis is concerned with the process of automatically aligning 3D medical images of human brain. It concentrates on rigid-body matching of Positron Emission Tomography images (PET) and Magnetic Resonance images (MR) within one patient and on non-linear matching of PET images of different patients. In recent years, mutual information has proved to be an excellent criterion for automatic registration of intra-individual images from different modalities. We propose and evaluate a method that combines a multi-resolution optimization of mutual information with an efficient segmentation of background voxels and a modified principal axes algorithm. We show that an acceleration factor of 6-7 can be achieved without loss of accuracy and that the method significantly reduces the rate of unsuccessful registrations. Emphasis was also laid on creation of an automatic registration system that could be used routinely in clinical environment. Non-linear registration tries to reduce the inter-individual variability of shape and structure between two brain images by deforming one image so that homologous regions in both images get aligned. It is an important step of many procedures in medical image processing and analysis. We present a novel algorithm for an automatic non-linear registration of PET images based on hierarchical volume subdivisions and local affine optimizations. It produces a C2-continuous deformation function and guarantees that the deformation is one-to-one. Performance of the algorithm was evaluated on more than 600 clinical PET images

Development and evaluation of biomarkers in Huntington’s Disease: furthering our understanding of the disease and preparing for clinical trials

Huntington’s Disease (HD) is a devastating hereditary neurodegenerative disease for which there are currently only symptomatic treatments. Several potentially curative pharmaceutical and genetic therapies are however in varying stages of development and therefore an increasing number of large-scale clinical trials of disease-modifying therapies are imminent. There is consequently a need for biomarkers which are sensitive to beneficial attenuation of disease-related changes. Functional, neuroimaging and biochemical biomarkers have been developed in HD (Andre et al. 2014;Weir et al. 2011). Neuroimaging biomarkers are strong candidates based on their clear relevance to the neuropathology of disease, proven precision and superior sensitivity compared with some standard functional measures (Tabrizi et al. 2011;Tabrizi et al. 2012). Their use in early-stage clinical trials, as surrogate end-points providing initial evidence of biological effect, is becoming increasingly common. Comparison of biomarkers in HD will help to clarify which measures, over varying time intervals, are most sensitive to disease progression. Additionally, the identification of robust fully-automated methods, comparable to manual and semi-automated gold-standards, would facilitate large-scale volumetric analysis. These methods however require validation in observational studies of neurodegenerative disease before they can be applied to sensitive clinical trial data. This thesis will develop and evaluate biomarkers for use in HD; both furthering our understanding of the disease and in preparation for use as end-points in clinical trials. A direct comparison of the sensitivity of diffusion and volumetric imaging biomarkers to HD-related change will be reported for the first time. Several exploratory imaging investigations are also described which enhance current knowledge of the relationship between neuroimaging metrics, brain functioning and behaviour, additionally strengthening the argument for the clinical relevance of neuroimaging measures as surrogate end-points in HD. The thesis will conclude with a comprehensive biomarker evaluation in early-stage HD, along with suggested strategies for selection of primary and secondary trial end-points based on effect sizes and corresponding sample size requirements

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

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

The selective updating of working memory: a predictive coding account

Goal-relevant information maintained in working memory is remarkably robust and resistant to distractions. However, our nervous system is endowed with exceptional flexibility; therefore such information can be updated almost effortlessly. A scenario – not uncommon in our daily life – is that selective maintaining and updating information can be achieved concurrently. This is an intriguing example of how our brain balances stability and flexibility, when organising its knowledge. A possibility – one may draw upon to understand this capacity – is that working memory is represented as beliefs, or its probability densities, which are updated in a context-sensitive manner. This means one could treat working memory in the same way as perception – i.e., memories are based on inferring the cause of sensations, except that the time scale ranges from an instant to prolonged anticipation. In this setting, working memory is susceptible to prior information encoded in the brain’s model of its world. This thesis aimed to establish an interpretation of working memory processing that rests on the (generalised) predictive coding framework, or hierarchical inference in the brain. Specifically, the main question it asked was how anticipation modulates working memory updating (or maintenance). A novel working memory updating task was designed in this regard. Blood-oxygen-level dependent (BOLD) imaging, machine learning, and dynamic causal modelling (DCM) were applied to identify the neural correlates of anticipation and the violation of anticipation, as well as the causal structure generating these neural correlates. Anticipation induced neural activity in the dopaminergic midbrain and the striatum. Whereas, the fronto-parietal and cingulo-operculum network were implicated when an anticipated update was omitted, and the midbrain, occipital cortices, and cerebellum when an update was unexpected. DCM revealed that anticipation is a modulation of backward connections, whilst the associated surprise is mediated by forward and local recurrent modulations. Two mutually antagonistic pathways were differentially modulated under anticipatory flexibility and stability, respectively. The overall results indicate that working memory may as well follow the cortical message-passing scheme that enables hierarchical inference