This thesis contains two studies. The first study consisted of a prospective, randomized, clinical trial involving twenty-five client-owned dogs with intracranial disease. Each dog was randomly assigned to receive propofol or isoflurane for maintenance of anesthesia, without premedication. All dogs received propofol IV to effect, were intubated and mechanically ventilated (end-tidal carbon dioxide [ETCO2] 30-35 mmHg). Temperature and cardiac output were measured pre- and post-magnetic resonance imaging (MRI). Scores for mentation, neurological status, maintenance, and recovery were obtained. Pulse oximetry, end tidal gases, arterial blood pressure (AP), heart rate (HR) and requirements for dopamine administration to maintain mean AP > 60 mmHg were recorded throughout anesthesia. Cardiac index was higher, while HR was lower, with propofol in dogs younger than 5 years. Dogs receiving isoflurane were 14.7 times more likely to require dopamine. Sedation and maintenance scores and temperature were not different. Mean and diastolic AP were higher in the propofol group. Recovery scores were better with propofol. Change in neurological score from pre- to post-anesthesia was not different between treatments. In the second study, blood and CSF were collected from 11 dogs with intracranial disease after MRI (Group ID-MRI), in 10 healthy dogs post-MRI (Group H-MRI), and in 39 healthy dogs after induction of anesthesia (Group H-Anesth). Groups ID-MRI and H-MRI were induced to anesthesia with propofol, IV to effect, and maintained on isoflurane or propofol. Dogs in H-Anesth were premedicated with acepromazine and hydromorphone, induced with propofol or thiopental, IV to effect, and maintained on isoflurane. Neurologic scores (NS) and sedation scores (SS) were assessed pre-anesthesia in ID-MRI dogs. There was a tendency for higher cerebrospinal fluid lactate (CSFL) in ID-MRI than H-MRI or H-Anesth (p = 0.12). There was agreement between CSFL and plasma lactate (PL) in ID-MRI dogs (p = 0.007), but not in H-MRI (p = 0.45) or H-Anesth (p = 0.15). Of the ID-MRI dogs, those with worse NS had higher CSFL (r2 = 0.44). Propofol showed some advantages to isoflurane in this patient population for maintenance of blood pressure and recovery. The results of the second study warrant further investigation. / OVC Pet Trust

As humans age, specific mental faculties deteriorate even in the absence of dementia. Age related cognitive decline affects quality of life, and has significant implications from a socio-economic perspective; however not everyone declines to equal degrees, at equal rates, or from the same baseline. This PhD examined a large sample of community-dwelling older adults called the Lothian Birth Cohort 1936, most of whom completed an intelligence test at age 11 years, and again around age 73 as part of a detailed assessment that also included detailed brain magnetic resonance imaging (N range = 700-866). I investigated the independent effects of two linked genetic loci which have been associated with greater risk of Alzheimer’s disease – the APOE ε haplotype (commonly ‘genotype’) and a poly-T repeat in the TOMM40 gene. Are 'risk' variants in these loci associated with specific measures of cognitive ageing and brain structure - specifically white matter microstructural integrity, hippocampal volumes, white matter lesions or cerebral microbleeds – in this sample? Firstly, a pilot study aimed to replicate significant associations between the ADRB2 gene and brain imaging/cognitive phenotypes, that had previously been reported in a smaller subsample of the cohort that had by that time undergone MRI (n = 132). Previously reported significant associations were not significant in the larger, full LBC1936 sample (n = 700-866), but novel significant associations were found (P < 0.05). Specifically, integrity of the left arcuate fasciculus white matter tract significantly mediated part of the association between specific genetic variations at ADRB2, and the Digit Symbol Coding task of information processing speed. These findings indicated that this approach – testing three-way genetic/brain imaging/cognitive associations for mediation - was viable for the main APOE/TOMM40 analyses. Results in the main APOE/TOMM40 analyses showed that specific variants in the APOE and TOMM40 gene loci were statistically significantly associated (at raw P value <0.05) with white matter tract microstructural integrity, but not white matter lesions, hippocampal volume or cerebral microbleeds. Inconsistencies with previous, positive reports showing significant associations between APOE ε and these latter phenotypes may reflect a degree of type 1 error or more study-specific discrepancies (which are detailed throughout). APOE ε was significantly associated with average scores on a large proportion of cognitive tests, independent of age 11 intelligence (i.e. ‘cognitive ageing’; Deary et al., 2004). These associations were partly – but not completely – mediated by white matter tract microstructural integrity. TOMM40 poly-T repeat genotype was associated with cognitive ageing to a much lesser extent. A range of brain phenotypes may form the anatomical basis for significant associations between APOE genotype and cognitive ageing, among which includes white matter tract microstructural integrity.

Background: Volumetric analysis of brain structures from structural Mag- netic Resonance (MR) images advances the understanding of the brain by providing means to study brain morphometric changes quantitatively along aging, development, and disease status. Due to the recent increased emphasis on large-scale multicenter brain MR study design, the demand for an automated brain MRI processing tool has increased as well. This dissertation describes an automatic segmentation framework for subcortical structures of brain MRI that is robust for a wide variety of MR data. Method: The proposed segmentation framework, BRAINSCut, is an inte- gration of robust data standardization techniques and machine-learning approaches. First, a robust multi-modal pre-processing tool for automated registration, bias cor- rection, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. The segmentation framework was then constructed to achieve robustness for large-scale data via the following comparative experiments: 1) Find the best machine-learning algorithm among several available approaches in the field. 2) Find an efficient intensity normalization technique for the proposed region-specific localized normalization with a choice of robust statistics. 3) Find high quality features that best characterize the MR brain subcortical structures. Our tool is built upon 32 handpicked multi-modal muticenter MR images with man- ual traces of six subcortical structures (nucleus accumben, caudate nucleus, globus pallidum, putamen, thalamus, and hippocampus) from three experts. A fundamental task associated with brain MR image segmentation for re- search and clinical trials is the validation of segmentation accuracy. This dissertation evaluated the proposed segmentation framework in terms of validity and reliability. Three groups of data were employed for the various evaluation aspects: 1) traveling human phantom data for the multicenter reliability, 2) a set of repeated scans for the measurement stability across various disease statuses, and 3) a large-scale data from Huntington's disease (HD) study for software robustness as well as segmentation accuracy. Result: Segmentation accuracy of six subcortical structures was improved with 1) the bias-corrected inputs, 2) the two region-specific intensity normalization strategies and 3) the random forest machine-learning algorithm with the selected feature-enhanced image. The analysis of traveling human phantom data showed no center-specific bias in volume measurements from BRAINSCut. The repeated mea- sure reliability of the most of structures also displayed no specific association to disease progression except for caudate nucleus from the group of high risk for HD. The constructed segmentation framework was successfully applied on multicenter MR data from PREDICT-HD [133] study ( < 10% failure rate over 3000 scan sessions pro- cessed). Conclusion: Random-forest based segmentation method is effective and robust to large-scale multicenter data variation, especially with a proper choice of the intensity normalization techniques. Benefits of proper normalization approaches are more apparent compared to the custom set of feature-enhanced images for the ccuracy and robustness of the segmentation tool. BRAINSCut effectively produced subcortical volumetric measurements that are robust to center and disease status with validity confirmed by human experts and low failure rate from large-scale multicenter MR data. Sample size estimation, which is crutial for designing efficient clinical and research trials, is provided based on our experiments for six subcortical structures.

Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a gradually progressive degenerative neurological disorder that is characterized by increasing cognitive impairment, characteristic degenerative pathology and brain atrophy. Studies have shown that the progression of AD pathology in the brain develops in a predictable pattern and the pathological changes that take place in brain begin at the microscopic level long before the first signs of memory loss. Structural Magnetic Resonance Imaging (MRI), which has exceptional soft tissue contrast and detailed resolution, is the best way to noninvasively examine changes which occur early in the course of AD. For this dissertation, our aim is to improve the methods for measuring the atrophy of brain structures in AD, as seen on MRI, and to apply these methods to subjects with cognitive impairment. This study has established a new coordinate template to replace the widely used Montreal Neurological Institute (MNI) template for the atlas-based segmentation procedure. The new template was derived from the same structural image as the one used by the Automated Anatomical Labeling (AAL) procedure. The agreement of the newly developed coordinate template and AAL helps to estimate accurate spatial transformation parameters used in warping the AAL to individual subject images. The new template combines the spatial information of the structural image and the frequency information of MNI template. Based on the same principle, a set of customized templates has been developed. The customized template, associated atlas and customized priors match more closely the aging population than the previous template, so as to improve the atlas-based segmentation of regions of interest in AD assessment. Visual Rating System (VRS) of a single coronal slice (MB slice) in MRI has been another valuable method in the assessment of medial temporal lobe atrophy. An automated procedure has been developed in this study to measure the hippocampal area on the same coronal slice so that the labor of human experts in the VRS assessment of hippocampus will be significantly reduced. Finally the methods and materials (template and atlas) developed in this dissertation were applied to cross-sectional studies of subjects with cognitive impairment. We conducted volumetric analysis on subjects and conclude that the data from the new approaches have higher correlations with clinical data, and therefore can be reliably used as part of an AD assessment tool.

In this thesis, we aim to investigate the correlation between myelination and the gestational age for preterm infants, with the former being an important developmental process during human brain maturation. Quantification of myelin requires dedicated imaging, but the conventional magnetic resonance images routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. This thesis thus focuses on structural segmentation and spatio-temporal modelling of the so-called myelin-like signals on T2-weighted scans for early prognostic evaluation of the preterm brain. The segmentation part poses the major challenges of this task: insufficient spatial prior information of myelination and the presence of substantial partial volume voxels in clinical data. Specific spatial priors for the developing brain are obtained from either probabilistic atlases or manually annotated training images, but none of them currently include myelin as an individual tissue type. This causes further difficulties in partial volume estimation which depends on the probabilistic atlases of the composing pure tissues. Our key contribution is the development of an expectation-maximisation framework that incorporates an explicit partial volume class whose locations are configured in relation to the composing pure tissues in a predefined region of interest via second-order Markov random fields. This approach resolves the above challenges without requiring any probabilistic atlas of myelin. We also investigate atlas-based whole brain segmentation that generates the binary mask for the region of interest. We then construct a spatio-temporal growth model for myelin-like signals using logistic regression based on the automatic segmentations of 114 preterm infants aged between 29 and 44 gestational weeks. Lastly, we demonstrate the ability of age estimation using the normal growth model in a leave-one-out procedure.

Advances in MR technology have improved the potential for visualization of small lesions in brain images. This has resulted in the opportunity to detect cerebral microbleeds (CMBs), small hemorrhages in the brain that are known to be associated with risk of ischemic stroke and intracerebral bleeding. Currently, no computerized method is available for fully- or semi-automated detection of CMBs. In this paper, we propose a CAD system for the detection of CMBs to speed up visual analysis in population-based studies. Our method consists of three steps: (i) skull-stripping (ii) initial candidate selection (iii) reduction of false-positives using a two layer classi cation and (iv) determining the anatomical location of CMBs. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The geometrical, intensity-based and local image descriptor features were used in the classi cation steps. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The sensitivity for CMB detection was 90% with, on average, 4 false-positives per subject.

L’essor de l’imagerie médicale par résonance magnétique (IRM) permet une exploration de plus en plus précise du cerveau en période néonatale. Comment interpréter le plus objectivement possible des images dont les particularités compliquent l’analyse ? La controverse autour des hyperintensités diffuses de la substance blanche (diffuse excessive high signal intensity, DEHSI) en est une illustration. Le premier objectif est d’étudier la variabilité des appréciations des radiologues. Il existe une bonne reproductibilité des mesures bidimensionnelles des structures cérébrales, mais une reproductibilité intra et inter-observateurs moyenne de l’analyse visuelle de l’intensité de signal de la substance blanche néonatale. Le second objectif est le développement d’une méthode de segmentation utilisant des outils de traitement d’images, essentiellement morphologiques, en particulier des opérateurs connexes. Elle permet de segmenter la substance grise, la substance blanche et le liquide cérébro-spinal à l’étage sus-tentoriel et détecter automatiquement la présence d’hyperintensités de la substance blanche. Une mesure normalisée de la sévérité de celles-ci par rapport à la substance blanche adjacente est calculée, ce qui constitue une contribution originale de la thèse. La validation des résultats sur des images acquises avec des champs magnétiques de 1,5 et 3 T par comparaison à des segmentations manuelles autorise l’utilisation de ce logiciel. La confrontation des résultats obtenus au suivi clinique à long terme de nouveau-nés permettra de mieux connaître et interpréter le développement cérébral visualisé en IRM et d’apporter une réponse face au défi que constituent les DEHSI. / Progress in magnetic resonance imaging (MRI) has allowed more detailed exploration of the development and maturation of the neonatal brain. Among the challenges facing radiologists are determining how best to objectively analyze images with very different characteristics when compared to older children. One issue is the “diffuse excessive high signal intensity” (DEHSI) of the white matter in premature newborns, whose definition, classification and prognosis have been vigorously debated. The role played in this analysis by the subjectivity of the radiological interpretation is not well understood. Our primary objective was to study the variability of this subjective analysis by the radiologist. Although reproducibility is acceptable for bi-dimensional measurement of brain structures, it is only fair for the analysis of signal intensity of brain white matter. The secondary objective was the design of a robust and reliable semi-automatic method to segment the gray matter, the white matter, and the cerebrospinal fluid and detect potential high signal intensity regions (it calculates a normalized mean value, and compares it to the normal surrounding white matter.). The algorithm is composed of an isotropic diffusion filter, morphological tools and connected operators, all implemented in a software interface. The results of this algorithm have been validated on MRI images acquired on 1.5 and 3 T devices by comparing them with segmentation results. This new tool could be employed in routine MRI. Correlation of the results with clinical outcomes in infants would permit a better understanding of cerebral development and, particularly, elucidate the significance of DEHSI.

Polymicrogyria (PMG) is one brain disease that mainly occurs in the pediatric brain. Heavy PMG will cause seizures, delayed development, and a series of problems. For this reason, it is critical to effectively identify PMG and start early treatment. Radiologists typically identify PMG through magnetic resonance imaging scans. In this study, we create and open a pediatric MRI dataset (named PPMR dataset) including PMG and controls from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The difference between PMG MRIs and control MRIs is subtle and the true distribution of the features of the disease is unknown. Hence, we propose a novel center-based deep contrastive metric learning loss function (named cDCM Loss) to deal with this difficult problem. Cross-entropy-based loss functions do not lead to models with good generalization on small and imbalanced dataset with partially known distributions. We conduct exhaustive experiments on a modified CIFAR-10 dataset to demonstrate the efficacy of our proposed loss function compared to cross-entropy-based loss functions and the state-of-the-art Deep SAD loss function. Additionally, based on our proposed loss function, we customize a deep learning model structure that integrates dilated convolution, squeeze-and-excitation blocks and feature fusion for our PPMR dataset, to achieve 92.01% recall. Since our suggested method is a computer-aided tool to assist radiologists in selecting potential PMG MRIs, 55.04% precision is acceptable. To our best knowledge, this research is the first to apply machine learning techniques to identify PMG only from MRI and our innovative method achieves better results than baseline methods.

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