An insight into the brain of patients with type-2 diabetes mellitus and impaired glucose tolerance using multi-modal magnetic resonance image processing

The purpose of this thesis was to investigate brain anatomy and physiology of subjects with impaired glucose tolerance (IGT - 12 subjects), type-2 diabetes (T2DM - 17 subjects) and normoglycemia (16 subjects) using multi-modal magnetic resonance imaging (MRI) at 3T. Perfusion imaging using quantitative STAR labeling of arterial regions (QUASAR) arterial spin labeling (ASL) was the core dataset. Optimization of the post-processing methodology for this sequence was performed and the outcome was used for hemodynamic analysis of the cohort. Typical perfusion-related parameters, along with novel hemodynamic features were quantified. High-resolution structural, angiographic and carotid flow scans were also acquired and processed. Functional acquisitions were repeated following a vasodilating stimulus. Differences between the groups were examined using statistical analysis and a machine-learning framework. Hemodynamic parameters differing between the groups emerged from both baseline and post-stimulus scans for T2DM and mainly from the post-stimulus scan for IGT. It was demonstrated that quantification of not-typically determined hemodynamic features could lead to optimal group-separation. Such features captured the pattern of delayed delivery of the blood to the arterial and tissue compartments of the hyperglycemic groups. Alterations in gray and white matter, cerebral vasculature and carotid blood flow were detected for the T2DM group. The IGT cohort was structurally similar to the healthy cohort but demonstrated functional similarities to T2DM. When combining all extracted MRI metrics, features driving optimal separation between different glycemic conditions emerged mainly from the QUASAR scan. The only highly discriminant non-QUASAR feature, when comparing T2DM to healthy subjects, emerged from the cerebral angiogram. In this thesis, it was demonstrated that MRI-derived features could lead to potentially optimal differentiation between normoglycemia and hyperglycemia. More importantly, it was shown that an impaired cerebral hemodynamic pattern exists in both IGT and T2DM and that the IGT group exhibits functional alterations similar to the T2DM group

Imaging mouse models of neurodegeneration using multi-parametric MRI

Alzheimer’s disease (AD) is a devastating condition characterised by significant cognitive impairment and memory loss. Transgenic mouse models are increasingly being used to further our knowledge of the cause and progression of AD, and identify new targets for therapeutic intervention. These mice permit the study of specific pathological hallmarks of the disease, including intracellular deposits of hyperphosphorylated tau protein and extracellular amyloid plaques. In order to characterise these transgenic mice, robust biomarkers are required to evaluate neurodegenerative changes and facilitate preclinical evaluation of emerging therapeutics. In this work, a platform for in vivo structural imaging of the rTg4510 mouse model of tauopathy was developed and optimised. This was combined with a range of other clinically relevant magnetic resonance imaging (MRI) biomarkers including: arterial spin labelling, diffusion tensor imaging and chemical exchange saturation transfer. These techniques were applied in a single time-point study of aged rTg4510 mice, as well as a longitudinal study to serially assess neurodegeneration in the same cohort of animals. Doxycycline was administered to a subset of rTg4510 mice to suppress the tau transgene; this novel intervention strategy permitted the evaluation of the sensitivity of MRI biomarkers to the accumulation and suppression of tau. Follow-up ex vivo scans were acquired in order to assess the sensitivity of in vivo structural MRI to the current preclinical gold standard. High resolution structural MRI, when used in conjunction with advanced computational analysis, yielded high sensitivity to pathological changes occurring in the rTg4510 mouse. Atrophy was reduced in animals treated with doxycycline. All other MRI biomarkers were able to discriminate between doxycycline-treated and untreated rTg4510 mice as well as wildtype controls, and provided insight into complimentary pathological mechanisms occurring within the disease process. In addition, this imaging protocol was applied to the J20 mouse model of familial AD. This mouse exhibits widespread plaque formation, enabling the study of amyloid-specific pathological changes. Atrophy and deficits in cerebral blood flow were observed; however, the changes occurring in this model were markedly less than those observed in the rTg4510 mouse. This study was expanded to investigate the early-onset AD observed in individuals with Down’s syndrome (DS) by breeding the J20 mouse with the Tc1 mouse model of DS, permitting the relationship between genetics and neurodegeneration to be dissected. This thesis demonstrates the application of in vivo multi-parametric MRI to mouse models of neurodegeneration. All techniques were sensitive to pathological changes occurring in the models, and may serve as important biomarkers in clinical studies of AD. In addition, in vivo multi-parametric MRI permits longitudinal studies of the same animal cohort. This experimental design produces more powerful results, whilst contributing to worldwide efforts to reduce animal usage with respect to the 3Rs principles

From clinics to methods and back: a tale of amyloid-PET quantification

The in-vivo assessment of cerebral amyloid load is taking a leading role in the early differential diagnosis of neurodegenerative diseases. With the hopefully near introduction of disease-modifying drugs, we expect a paradigm shift in the current diagnostic pathway with an unprecedented surge in the request of exams and detailed analysis

Measurement of subtle blood-brain barrier disruption in cerebral small vessel disease using dynamic contrast-enhanced magnetic resonance imaging

Cerebral small vessel disease (SVD) is a common cause of strokes and dementia. The pathogenesis of SVD is poorly understood, but imaging and biochemical investigations suggest that subtle blood-brain barrier (BBB) leakage may contribute to tissue damage. The most widely-used imaging method for assessing BBB integrity and other microvascular properties is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI has primarily been applied in situations where contrast uptake in tissue is typically large and rapid (e.g. neuro-oncology); the optimal approach for quantifying BBB integrity in diseases where the BBB remains largely intact and the reliability of resulting measurements is unclear. The main purpose of this thesis was to assess and improve the reliability of quantitative assessment of subtle BBB disruption, in order to illuminate its potential role in cerebral SVD. Firstly, a systematic literature review was performed in order to provide an overview of DCE-MRI methods in the brain. This review found large variations in MRI procedures and data analysis methods, resulting in widely varying estimates of tracer kinetic parameters. Secondly, this thesis focused on the analysis of DCE-MRI data acquired in an on-site clinical study of mild stroke patients. After performing basic DCE-MRI processing (e.g. selection of a vascular input function), this work aimed to determine the tracer kinetic modelling approach most suitable for assessing subtle BBB disruption in this cohort. Using data-driven model selection and computer simulations, the Patlak model was found to provide accurate estimates of blood plasma volume and low-level BBB leakage. Thirdly, this thesis aimed to investigate two potential pitfalls in the quantification of subtle BBB disruption. Contrast-free measurements in healthy volunteers revealed that a signal drift of approximately 0.1 %/min occurs during the DCE-MRI acquisition; computer simulations showed that this drift introduces significant systematic errors when estimating low-level tracer kinetic parameters. Furthermore, tracer kinetic analysis was performed in an external patient cohort in order to investigate the inter-study comparability of DCE-MRI measurements. Due to the nature of the acquisition protocol it proved difficult to obtain reliable estimates of BBB leakage, highlighting the importance of study design. Lastly, this thesis examined the relationship between quantitative MRI parameters and clinical measurements in cerebral SVD, with a focus on the estimates of blood volume and BBB leakage obtained in the internal SVD patient cohort. This work did not provide evidence that BBB leakage in normal-appearing tissue increases with SVD burden or predicts disease progression; however, increased BBB leakage was found in white matter hyperintensities. Furthermore, this work raises the possibility of a role for blood plasma volume and dietary salt intake in cerebral SVD. The work described in this thesis has demonstrated that it is possible to estimate subtle BBB disruption using DCE-MRI, provided that the measurement and data analysis strategies are carefully optimised. However, absolute values of tracer kinetic parameters should be interpreted with caution, particularly when making comparisons between studies, and sources of error and their influence should be estimated where possible. The exact roles of BBB breakdown and other microvascular changes in SVD pathology remain to be defined; however, the work presented in this thesis contributes further insights and, together with technical advances, will facilitate improved study design in the future

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Artificial Intelligence with Light Supervision: Application to Neuroimaging

Recent developments in artificial intelligence research have resulted in tremendous success in computer vision, natural language processing and medical imaging tasks, often reaching human or superhuman performance. In this thesis, I further developed artificial intelligence methods based on convolutional neural networks with a special focus on the automated analysis of brain magnetic resonance imaging scans (MRI). I showed that efficient artificial intelligence systems can be created using only minimal supervision, by reducing the quantity and quality of annotations used for training. I applied those methods to the automated assessment of the burden of enlarged perivascular spaces, brain structural changes that may be related to dementia, stroke, mult