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

Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

3T magnetic resonance imaging of cortical grey matter lesions in Multiple Sclerosis

Radiological and histopathological studies have established that in addition to the classical white matter (WM) demyelinating lesions, cortical grey matter (CGM) lesions are also a significant part of the pathology in multiple sclerosis (MS), and contribute to the clinical and cognitive deficits seen in MS patients. Double inversion recovery (DIR) has been identified as a good sequence for the radiological detection of cortical grey matter lesions. In this project I investigated the role of phase sensitive inversion recovery (PSIR), a T1-weighted MRI sequence, using a 3T MRI scanner, for the detection of CGM lesions in multiple sclerosis. Detection of CGM lesions on a standard DIR sequence (1x1x3mm resolution) was compared with a higher resolution PSIR sequence (0.5x0.5x2mm), to explore if it can help improve the study of CGM lesions. A representative cohort, including patients with relapsing remitting (RR), primary progressive (PP) and secondary progressive (SP)MS, was recruited in this project, together with controls in order to allow a comparison of findings with those of healthy subjects who do not have MS. I systematically investigated if the use of high resolution PSIR scans can improve CGM lesion detection and classification, when compared to DIR. Using the PSIR sequence, I studied the hypothesis that the distribution of lesions impacts the pattern of cognitive impairment seen in patients. CGM lesion volumes were estimated for frontal, temporal , parietal and occipital lobes and cognitive tests were conducted (Hayling, Stroop, immediate and 4 delayed story and figure recall, PASAT (Paced auditory serial addition test) and SDMT (Symbol digit modality test)). Differences between phenotypes and associations with cognitive measures were explored using a multiple regression model. A follow up study was undertaken to understand how CGM lesions evolve with time and in order to explore potential specificity of PSIRdetected CGM lesions, I compared the findings of CGM lesion detection in MS patients, with patients diagnosed with Fabry’s disease. Compared with DIR, high resolution PSIR was found to detect a significantly greater number of CGM lesions and also improved the classification of CGM lesions. A fronto-temporal dominance of CGM lesions was noted in my study. Different CGM and JC lesion subtypes were found to be associated with cognitive function; the relationship being influenced by the lobar location and the cognitive function being assessed. In the follow-up study I found that people with SPMS have a grater accrual of CGM lesions than RRMS and the process appeared to be independent of WM lesion accrual. CGM lesions were also seen in patients with Fabry’s disease though the frequency was less that in MS. The data presented in my study suggests that PSIR has the potential to improve the quantitative and qualitative study of CGM lesions. CGM lesions were noted across all disease phenotypes, though more common in progressive disease. The distribution, accrual and evolution of CGM lesions provides insights into the pathogenesis of MS and helps understand the contribution of CGM lesions to neurological and cognitive impairment. Detection of CGM lesions has a potential role to help with a diagnosis of MS when it suspected but not confirmed

A data mining approach using cortical thickness for diagnosis and characterization of essential tremor.

Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects. We found that cortical thickness features alone distinguished the two, ET from controls, with 81% diagnostic accuracy. More specifically, roughness (i.e., the standard deviation of cortical thickness) of the right inferior parietal and right fusiform areas was shown to play a key role in ET characterization. Moreover, these features allowed us to identify subgroups of ET patients as well as healthy subjects at risk for ET. Since treatment of tremors is disease specific, accurate and early diagnosis plays an important role in tremor management. Supporting the clinical diagnosis with novel computer approaches based on the objective evaluation of neuroimage data, like the one presented here, may represent a significant step in this direction.post-print1720 K