This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

No description available.

Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, while the other involves the computational efficiency in classifying white matter tracts. The first key area that this dissertation focuses on is to implement a novel computing scheme for estimating regional white matter alterations along neural pathways in 3D space. The mechanism of the proposed method relies on white matter tractography and geodesic distance mapping. We propose a mask scheme to overcome the difficulty to reconstruct thin tract bundles. Real DTI data are employed to demonstrate the performance of the pro- posed technique. Experimental results show that the proposed method bears great potential to provide a sensitive approach for determining the white matter integrity in human brain. Another core objective of this work is to develop a class of new modeling and clustering techniques with improved performance and noise resistance for separating reconstructed white matter tracts to facilitate clinical group analysis. Different strategies are presented to handle different scenarios. For whole brain tractography reconstructed white matter tracts, a Fourier descriptor model and a clustering algorithm based on multivariate Gaussian mixture model and expectation maximization are proposed. Outliers are easily handled in this framework. Real DTI data experimental results show that the proposed algorithm is relatively effective and may offer an alternative for existing white matter fiber clustering methods. For a small amount of white matter fibers, a modeling and clustering algorithm with the capability of handling white matter fibers with unequal length and sharing no common starting region is also proposed and evaluated with real DTI data.

Diffusion Tensor Magnetic Resonance Imaging was used to examine the age-related changes in white matter structural integrity in the common chimpanzee. Fractional Anisotropy(FA), a measure derived from the diffusion tensor data is sensitive to developmental and pathological changes in axonal density, myelination, size and coherence of organization of fibers within a voxel and thus reflects the white matter structural integrity. There is substantial evidence that white matter structural integrity decreases with age in humans. The long-term goal of this study is to compare the age-related changes in the white matter structural integrity among humans and chimpanzess to provide potential insights into the unique features of human aging. Different methods, including Region Of Interest (ROI) analysis, Tract Based Spatial Statistics (TBSS) are used to describe age-related changes in FA in a group of 21 chimpanzees. Strengths and limitations of these methods were discussed.

Diffusion MRI provides a unique opportunity to study the brain tissue architecture at a microscopic level. More specifically, it allows to infer biophysical properties of the axons in the white matter in-vivo. Microstructural parameters are widely used in multi-subject studies to track pathological processes, follow normal development and aging, or investigate behaviour. This thesis aims to identify and potentially address the limitations and pitfalls in voxelwise comparison of diffusion MRI parameters across subjects. To allow for accurate matching of brain structures across subjects, non-linear transformation that spatially aligns the data is required. We demonstrate that using advanced registration methods, we can outperform the standard registration-projection approach both in terms of sensitivity and specificity. The coarse resolution of the data typically causes partial volume effects that bias the diffusion parameters and potentially mislead the interpretation of a group study outcome. We provide evidence that these effects can be addressed by constraining the diffusion model parameter space, which leads to marginally lower sensitivity, but allows an accurate interpretation of the results. Additionally, we suggest that additional information inferred with a data driven approach might mitigate the loss in sensitivity. Finally, we design an original tract-specific modelling framework that enables to estimate microstructural parameters unbiased by the presence of foreign fibre populations or tissues. We demonstrate the sensitivity of our method in a study relating microstructure and behaviour.

O transtorno bipolar (TB) é uma das doenças psiquiátricas mais comuns e com altas taxas de morbimortalidade. Existe uma busca de um modelo neurofisiológico que poderia fornecer medidas objetivas para o diagnóstico desta doença, bem como fornecer parâmetros fisiológicos para monitorar a progressão, quantificar o dano causado pela mesma e prever a resposta ao tratamento na tentativa de direcionar a terapêutica adequada a cada estágio e evitar sua progressão. Kapczinski et al (2014) propôs um modelo de estadiamento do TB baseado em sintomas interepisódio, biomarcadores (inflamatórios e neuroplásticos) e dano cognitivo. Na última década, técnicas de neuroimagem e de genética proliferaram e vem se tornando promissoras ferramentas para se estabelecer a base de modelos neurofisiológicos do TB. Embora o prejuízo cognitivo já esteja bem descrito na literatura, o conhecimento sobre as alterações de conectividade em estudos de neuroimagem associadas a esta condição ainda é escasso. A atual pesquisa se propos a investigar os mecanismos fisiopatológicos do transtorno do humor bipolar com enfoque na substância branca (SB) cerebral. Primeiramente foi feita uma revisão sistemática da literatura internacional de estudos que correlacionaram o TB e estudo de tensor de difusão (DTI) por ressonância magnética (RM). Foram incluidos 18 trabalhos que fechavam com os critérios da pesquisa. Os trabalhos mostraram nas análises de DTI alterações na integridade da SB entre o os pacientes com TB em comparação com os controles normais. Essas alterações foram encontradas especialmente nos tratos de SB implicados em conectar uma ampla gama de redes neurais, incluindo as regiões estriatais ventrais e fronto-temporais. A maioria dos estudos mostrou valores de anisotropia fracionada (FA) reduzidos em tratos comissurais inter-hemisféricos e de associação frontolímbicos, com destaque para o corpo caloso que foi a estrutura mais acometida nos diferentes estudos. Estes achados são concordantes com a "síndrome de desconexão" que é encontrada em pacientes com TB. O segundo propósito desta tese foi fazer uma comparação dos volumes de substância branca total, do volume do corpo caloso e do volume total de substância cinzenta entre pacientes com TB em estagio inicial e avançado em relação aos seus respectivos controles normais pareados para sexo, idade, hemisfério dominante e nível de escolaridade. A análise de volumetria das estruturas corticais e subcorticais foi feita por meio de método de segmentação automatizada pelo software Freesurfer. Foram avaliados 55 sujeitos, sendo 29 pacientes com TB e 26 controles normais. A análise volumétrica encontrou redução da SB total e do volume do corpo caloso tanto em pacientes com TB no estagio inicial quanto tardio da doença. O volume de susbtância cinzenta (SC) total estava reduzido somente nos pacientes com TB em estágio tardio. Estes achados são inéditos na literatura e podem explicar a síndrome desconectiva dos pacientes com TB desde os estágios iniciais e o declínio cognitivo acentuado dos pacientes em estágios mais avançados da doença. Podem propor uma pista para achados de biomarcadores em populações em risco para desenvolver TB. / Bipolar disorder (BD) is one of the most common psychiatric disorders and with high morbidity and mortality rates. There is a search for a neurophysiological model that could provide objective measurements for diagnosis of this disease, as well as providing physiological parameters to monitor the progression, quantify the damage caused by it and predict response to treatment in an attempt to direct the appropriate therapy for each stage and prevent its progression. Kapczinski et al. (2014) proposed a staging BD model based on interepisode symptoms, biomarkers (inflammatory and neuroplastic) and cognitive impairment. In the last decade, techniques of neuroimaging and genetic proliferated and are becoming promising tools to settle the basis of neurophysiological models of BD. Although cognitive impairment is already well described in the literature, knowledge of the connectivity changes in neuroimaging studies associated with this condition is still scarce. Current research is proposed to investigate the pathophysiological mechanisms of bipolar disorder focusing on white matter (WM) brain. It was first made a systematic review of the literature studies that correlated the BD and diffusion tensor imaging (DTI). We found 18 published DTI studies that identified WM changes in subjects with bipolar disorder. The studies showed changes in the integrity of DTI WM between BD patients compared with normal controls. These changes were found especially in the treatment of WM connecting implicated in a wide range of neural networks, including ventral striatal regions and frontotemporal. Most studies showed reduced FA values in commissural inter-hemispheric and fronto-limbic association tracts, especially the corpus callosum which was the most affected structure in different studies. These findings are consistent with the "disconnection syndrome" which is found in patients with TB. The second purpose of this thesis was to compare the total white matter volume, the corpus callosum volume and total volume of gray matter in patients with BD in early and advanced stage in relation to their normal controls matched for sex, age, dominant hemisphere and education level. The volumetric analysis of cortical and subcortical structures was performed by automated segmentation method by FreeSurfer software. We evaluated 55 subjects, 29 BD patients and 26 normal controls. Volumetric analysis found reduced total WM and the corpus callosum volume both in BD patients at the early stage and late disease. The volume of total gray matter (GM) was reduced only in patients with late-stage BD. These findings are unprecedented in the literature and may explain the disconnection syndrome that is present in patients from the early stages and the marked cognitive decline of patients in more advanced stages of the disease. These findings may offer a clue to biomarker findings in populations at risk to develop BD.

Dans cette thèse, nous présentons un système complet permettant de sauvegarder un processus destructif tel qu'une dissection anatomique. Nous proposons une méthode depuis l'acquisition 3D des données jusqu'à la visualisation interactive et immersive, dans le but de créer une vérité terrain. L'acquisition 3D regroupe l'acquisition de la géométrie par scanner laser (maillage) ainsi que de l'information de couleur par le biais d'un appareil photo haute résolution (texture). Ce processus d'acquisition et répété au cours de la dissection du spécimen. Les différentes acquisitions du spécimen sont représentées par des surfaces 3D texturées. Elles sont ensuite recalées entre elles. Un expert anatomiste peut alors explorer ces différentes étapes de dissections modélisées dans une visualisation immersive en utilisant du matériel d'interaction (bras haptique). Un outil d'étiquetage permet une segmentation manuelle précise de régions d'intérêt visibles sur chacune des surfaces 3D. Un objet tridimensionnel peut ensuite être reconstruit et proposé à l'utilisateur sur la base des zones d'intérêt étiquetées. Le but étant de créer des vérité terrains afin de confronter des résultats issus de modalités d'acquisition volumiques (IRM). Nous montrons l'application de la méthode à la reconstruction de faisceaux de fibres blanches humaine dans le but de valider des résultats de tractographie. / In this thesis, we present a system to keep track of a destructive process such as a medical specimen dissection, from data acquisition to interactive and immersive visualization, in order to build ground truth models. Acquisition is a two-step process, first involving a 3D laser scanner to get a 3D surface, and then a high resolution camera for capturing the texture. This acquisition process is repeated at each step of the dissection, depending on the expected accuracy and the specific objects to be studied. Thanks to fiducial markers, surfaces are registered on each others. Experts can then explore data using interaction hardware in an immersive 3D visualization. An interactive labeling tool is provided to the anatomist, in order to identify regions of interest on each acquired surface. 3D objects can then be reconstructed according to the selected surfaces. We aim to produce ground truths which for instance can be used to validate data acquired with MRI. The system is applied to the specific case of white fibers reconstruction in the human brain.

Preterm birth (defined as birth before 37 weeks) is a leading cause of neurocognitive impairment in childhood, including difficulties in social cognition and executive function. Microstructural divergence from typical brain development in the preterm brain can be quantified using diffusion magnetic resonance imaging (dMRI) tractography during the neonatal period. The relationship between dMRI tractography metrics and later cognitive difficulties remains inconclusive. A general measure of white matter microstructure (gWM) offers a neural basis for cognitive processes in adults, however it remains unclear when gWM is first detectable in the developmental trajectory. Eye-tracking is a technique which assesses eye-gaze behaviour in response to visual stimuli, which permits inference about underlying cognitive processes, such as social cognition and executive function in infancy. The primary aims of this thesis were to test the hypotheses: dMRI tractography reveals significant differences in tract-average fractional anisotropy (FA) and mean diffusivity (MD) between preterm and term infants, and variance in tract-average FA and MD is shared across major tracts. Secondly, infants born preterm have altered social cognition and executive function compared to term born peers, assessed by eye-tracking and finally, neonatal MRI gWM is associated with cognitive function in infancy. Preterm (birth weight ≤ 1500g) and term infants (born ≥ 37 weeks’ post-menstrual age [PMA]) were recruited and underwent a MRI scan at term equivalent age (between 38 - 42 weeks’ PMA) and an eye-tracking assessment six to nine months later. Preterm infants were assessed at two years using the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). dMRI tractography metrics were generated using probabilistic neighbourhood tractography (PNT) in eight pre-defined tracts-of-interest. Principal component analyses (PCA) were used to determine the correlations between the eight tracts-of-interest for four tract-averaged water diffusion parameters. dMRI metrics were compared to the eye-tracking performance and two year outcome data. Quantitative microstructural changes were identifiable within the preterm brain when compared to infants born at term. PCA revealed a single variable that accounts for nearly 50% of shared variance between tracts-of-interest, and all tracts showed positive loadings. Eye-tracking revealed group-wise differences in infant social cognition, attributable to preterm birth, but executive functions inferred from eye-tracking did not differ between groups. dMRI tractography metrics within the neonatal period did not relate to later outcome measures. This thesis shows that variance in dMRI parameters is substantially shared across white matter tracts of the developing brain and suggests that anatomical foundations of later intelligence are present by term equivalent age. Social cognition is altered by preterm birth, however social cognitive ability in infancy is independent of gWM.

Cerebral small vessel disease (SVD) is associated with increased risks of stroke and dementia, however the mechanisms remain unclear. Low cerebral blood flow (CBF) has long been suggested and accepted, but clinical evidence is conflicting. On the other hand, growing evidence suggests that increased intracranial pulsatility due to vascular stiffening might be an alternative mechanism. Pulse-gated phase-contrast MRI is an imaging technique that allows measuring of CBF contemporaneously with pulsatility in multiple vessels and cerebrospinal fluid (CSF) spaces. The overall aim of this thesis was to provide an overview of existing clinical evidence on both hypotheses, to test the reproducibility of CBF and pulsatility measures in phase-contrast MRI, and to explore the relationship between CBF and intracranial pulsatility and SVD features in a group of patients with minor stroke and SVD changes on brain imaging. I first systematically reviewed and meta-analysed clinical studies that have assessed CBF or intracranial pulsatility in SVD patients. There were 38 studies (n=4006) on CBF and 27 (n=3356) on intracranial pulsatility. Most were cross-sectional, and longitudinal studies were scarce. There were large heterogeneities in patient characteristics and indices used particularly for measuring and calculating pulsatility. Methods to reduce bias such as blinding and the expertise of structural image readers were generally poorly reported, and many studies did not account for the impact of confounding factors (e.g. age, vascular risk factors and disease severity) on CBF or pulsatility. Evidence for falling CBF predating SVD was not supported by longitudinal studies; high pulsatility in one large artery such as internal carotid arteries (ICA) or middle cerebral arteries might be related to SVD, but studies that measured arteries, veins and CSF in the same patients were very limited and the reliability of some pulsatility measures, especially in CSF, needs to be tested. In order to test the reproducibility of the CBF and intracranial pulsatility measures, I repeated 2D phase-contrast MRI scans of vessels and CSF on healthy volunteers during two visits. I also compared the ICA pulsatility index derived from the MRI flow waveform to that from the Doppler ultrasound velocity waveform in patients with minor stroke and SVD features. In 10 heathy volunteers (age 35.2±9.78 years), the reproducibility of CBF and vascular pulsatility indices was good, with within-subject coefficients of variability (CV) less than 10%; whereas CSF flow and pulsatility measures were generally less reproducible (CV > 20%). In 56 patients (age 67.8±8.27 years), the ICA pulsatility indices in Doppler ultrasound and MRI were acceptably well-correlated (r=0.5, p < 0.001) considering the differences in the two techniques. We carried out a cross-sectional study aiming to recruit 60 patients with minor stroke and SVD features. We measured CBF and intracranial pulsatility using phase-contrast MRI, as well as aortic augmentation index (AIx) using a SphygmoCor device. I first investigated the relationship between intracranial measures, and systemic blood pressure or aortic AIx, and then focused on how the intracranial haemodynamic measures related to two main SVD features (white matter hyperintensities (WMH) and perivascular spaces (PVS)). We obtained usable data from 56/60 patients (age 67.8±8.27 years), reflecting a range of SVD burdens. After the adjustment for age, gender, and history of hypertension, higher pulsatility in the venous sinuses was associated with lower diastolic blood pressure and lower mean arterial pressure (e.g. diastolic blood pressure on straight sinus pulsatility index (PI): β=-0.005, P=0.029), but not with aortic AIx. Higher aortic AIx was associated with low ICA PI (β=-0.011, P=0.040). Increased pulsatility in the venous sinuses, not low CBF, was associated with greater WMH volume (e.g. superior sagittal sinus PI: β=1.29, P=0.005) and more basal ganglia PVS (e.g. odds ratio=1.379 per 0.1 increase in superior sagittal sinus PI) after the adjustment for age, gender and blood pressure. The thesis is the first to summarise the literature on CBF and intracranial pulsatility in SVD patients, addressed the major limitations of current clinical studies of SVD, and also assessed CBF and intracranial pulsatility contemporaneously in well-characterised patients with SVD features. The overall results of the thesis challenge the traditional hypothesis of the cause and effect between low CBF and SVD, and suggest that increased cerebrovascular pulsatility, which might be due to intrinsic cerebral small vessel pathologies rather than just aortic stiffness, is important for SVD. More importantly, this pilot study also provides a reliable methodology for measuring intracranial pulsatility using phase-contrast MRI for future longitudinal or larger multicentre studies, and shows that intracranial pulsatility could be used as a secondary outcome in clinical trials of SVD. However, future research is required to elucidate the implication of venous pulsatility and to fully explore the passage of pulse wave transmission in the brain. Overall, this thesis advances knowledge and suggest potential targets for future SVD studies in terms of mechanisms, prevention and treatment.