New MR imaging techniques in epilepsy

This thesis is concerned with the application of three magnetic resonance (MR) techniques in epilepsy: i.) Fluid attenuated inversion recovery prepared (FLAIR) imaging, ii.) diffusion imaging including diffusion tensor imaging (DTI) and iii.) serial and high resolution imaging of the hippocampus. I assessed the clinical value of fast FLAIR in epilepsy in a study involving 128 patients and of 3D FLAIR in a study involving 10 patients. The conspicuity of neocortical lesions and hippocampal sclerosis was increased. New lesions were detected in 5% of patients. The extent of low grade tumours was best assessed on 3D fast FLAIR images. Fast FLAIR was inferior to standard MR techniques for identifying and heterotopia. I applied newly developed, experimental diffusion imaging techniques. In eight studies using different diffusion imaging techniques involving a total of 50 patients and 54 control subjects I investigated the mobility of water molecules in the human epileptic brain in vivo. I used spin echo diffusion imaging in two studies, echo planar imaging (EPI) based DTI in four studies and EPI diffusion imaging in a patient during focal status epilepticus. Finally, in a preliminary study I attempted to use EPI diffusion imaging as a contrast to visualise transient changes associated with frequent lateralizing spikes. Our findings were: i.) diffusion is increased in hippocampal sclerosis suggesting a loss of structural organization and expansion of the extracellular space, ii.) displaying the directionality (anisotropy) of diffusion is superior to standard imaging to visualise tracts, iii.) anisotropy is reduced in the pyramidal tract in patients with hemiparesis and iv.) in the optic radiation in patients with hemianopia after temporal lobectomy suggesting wallerian degeneration, v.) both developmental and acquired structural abnormalities have a lower anisotropy than normal white matter, vi.) diffusion abnormalities in blunt head trauma are widespread and may include regions which are normal on standard imaging, indicating micro structural damage suggestive of diffuse axonal injury, vii.) focal status epilepticus can be associated with a reduced difflision in the affected cortex, viii.) diffusion imaging may be useful as a contrast for event-related (spike triggered) functional MR imaging. With serial MRI I demonstrated hippocampal volume loss in a patient after generalized status epilepticus and with high resolution imaging of an anatomical specimen and a control subject I showed hippocampal layers on MR images. The results presented in this thesis emphasised the flexibility of MR imaging and its ability to demonstrate abnormalities in vivo. FLAIR imaging is now part of the clinical work up of patients with epilepsy. Diffusion imaging has been shown to be superior to standard imaging to visualise tracts which has far-reaching implications for neurological applications. Diffusion imaging also provides an exciting window to study cerebral micro structure in vivo. Serial imaging allows for the first time the visualisation of temporal changes and high resolution imaging has the prospect of demonstrating hippocampal layers in vivo. MR imaging is a constantly progressing technique. It is hoped that this thesis will help to formulate hypotheses for new MR experiments to study the relationship of dysfunction and structural abnormalities

Doctor of Philosophy

dissertationMagnetic Resonance (MR) is a relatively risk-free and flexible imaging modality that is widely used for studying the brain. Biophysical and chemical properties of brain tissue are captured by intensity measurements in T1W (T1-Weighted) and T2W (T2-Weighted) MR scans. Rapid maturational processes taking place in the infant brain manifest as changes in co{\tiny }ntrast between white matter and gray matter tissue classes in these scans. However, studies based on MR image appearance face severe limitations due to the uncalibrated nature of MR intensity and its variability with respect to changing conditions of scan. In this work, we develop a method for studying the intensity variations between brain white matter and gray matter that are observed during infant brain development. This method is referred to by the acronym WIVID (White-gray Intensity Variation in Infant Development). WIVID is computed by measuring the Hellinger Distance of separation between intensity distributions of WM (White Matter) and GM (Gray Matter) tissue classes. The WIVID measure is shown to be relatively stable to interscan variations compared with raw signal intensity and does not require intensity normalization. In addition to quantification of tissue appearance changes using the WIVID measure, we test and implement a statistical framework for modeling temporal changes in this measure. WIVID contrast values are extracted from MR scans belonging to large-scale, longitudinal, infant brain imaging studies and modeled using the NLME (Nonlinear Mixed Effects) method. This framework generates a normative model of WIVID contrast changes with time, which captures brain appearance changes during neurodevelopment. Parameters from the estimated trajectories of WIVID contrast change are analyzed across brain lobes and image modalities. Parameters associated with the normative model of WIVID contrast change reflect established patterns of region-specific and modality-specific maturational sequences. We also detect differences in WIVID contrast change trajectories between distinct population groups. These groups are categorized based on sex and risk/diagnosis for ASD (Autism Spectrum Disorder). As a result of this work, the usage of the proposed WIVID contrast measure as a novel neuroimaging biomarker for characterizing tissue appearance is validated, and the clinical potential of the developed framework is demonstrated

Understanding progression in primary progressive multiple sclerosis: a longitudinal clinical and magnetic resonance imaging study

The work in this thesis applies magnetization transfer imaging (MTI) and conventional MRI measures (brain volume, T2 lesion load and enhancing lesions) to investigate the mechanisms underlying progression in primary progressive multiple sclerosis (PPMS), and identifies MR markers to predict and monitor progression. First, we demonstrated that MTI was sensitive to change in the normal appearing brain tissues over one year, and that clinical progression over this period was predicted by baseline normal appearing white matter (NAWM) MT ratio (MTR). However, our second study showed that over three years, grey matter MTR became a better predictor of progression than any other MRI measure. Grey matter MTR and T2 lesion load changes reflected concurrent progression during this study. To localize the baseline grey matter injury more precisely, we developed a voxelbased technique to identify areas of grey matter MTR reduction and volume loss in patients compared with controls. The regions of grey matter MTR reduction identified correlated with clinical function in anatomically related systems. Finally, because our studies showed that lesion load influenced progression, we used contrast enhanced T1-weighted imaging to examine active focal inflammation. We found that while lesion activity declined over five years, levels of activity at the start of the study could influence mobility five years later. The work presented in this thesis suggests that grey matter damage has a predilection for certain brain regions and is an important determinant of progression in early PPMS. In the white matter, changes in lesion volume and activity continue to influence progression, but NAWM injury may have a declining role. MTR is a sensitive and responsive tool for predicting, monitoring, and localizing clinically relevant brain injury in early PPMS

Machine Learning and its Application in Automatic Change Detection in Medical Images

Change detection is a fundamental problem in various fields, such as image surveillance, remote sensing, medical imaging, etc. The challenge of change detection in medical images is to detect disease-related changes while rejecting changes caused by noise, patient position change, and imaging acquisition artifacts such as field inhomogeneity. In this thesis, first, we overview the existing change detection methods, their underlying mathematical frameworks and limitations. Second, we present our contributions in solving the problem. We design optimal subspaces to approximate the background image in more efficient fashion. This is based on our structure principal component analysis, aiming to capture the structural similarity between scans in the context of change detection. We theoretically and numerically discuss the proper choices of norms used in the subspace approximation. The mathematical frameworks developed in this thesis consist of: (i) a new mathematical model to change detection by defining it as an optimization problem involving a cost function, input and output image sets, projection onto a subspace, and a similarity measure; (ii) development and implementation of numerical pipelines to compute the clinical changes by designing four mathematical algorithms; (iii) refining our algorithms by introducing the co-registration step utilizing the local dictionaries; and (iv) two new structure subspace learning models that are robust to outliers and noise, reduce the dimensionality of the dataset, and computationally efficient. We defined the co-registration step as a minimization problem involving a cost function, input and output image sets, a set of transform functions, projection onto a subspace, and a similarity measure. Based on the mathematical frameworks discussed above, numerical schemes are developed to automatically filter out clinically unrelated changes and identify true structure changes that may be of clinical importance. Our approaches are data-driven and utilize the knowledge of machine learning. We quantitatively analyze the performance of these algorithms using both synthetic and real human data. Our work has the potential to be used in computer aided diagnosis

Automated detection of lupus white matter lesions in MRI

Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration

Assessment and optimisation of MRI measures of atrophy as potential markers of disease progression in multiple sclerosis

There is a need for sensitive measures of disease progression in multiple sclerosis (MS) to monitor treatment effects and understand disease evolution. MRI measures of brain atrophy have been proposed for this purpose. This thesis investigates a number of measurement techniques to assess their relative ability to monitor disease progression in clinically isolated syndromes (CIS) and early relapsing remitting MS (RRMS). Presented, is work demonstrating that measurement techniques and MR acquisitions can be optimised to give small but significant improvements in measurement sensitivity and precision, which provided greater statistical power. Direct comparison of numerous techniques demonstrated significant differences between them. Atrophy measurements from SIENA and the BBSI (registration-based techniques) were significantly more precise than segmentation and subtraction of brain volumes, although larger percentage losses were observed in grey matter fraction. Ventricular enlargement (VE) gave similar statistical power and these techniques were robust and reliable; scan-rescan measurement error was <0.01% of brain volume for BBSI and SIENA and <0.04ml for VE. Annual atrophy rates (using SIENA) were -0.78% in RRMS and -0.52% in CIS patients who progressed to MS, which were significantly greater than the rate observed in controls (-0.07%). Sample size calculations for future trials of disease-modifying treatments in RRMS, using brain atrophy as an outcome measure, are described. For SIENA, the BBSI and VE respectively, an estimated 123, 157 and 140 patients per treatment arm respectively would be required to show a 30% slowing of atrophy rate over two years. In CIS subjects brain atrophy rate was a significant prognostic factor, independent of T2 MRI lesions at baseline, for development of MS by five year follow-up. It was also the most significant MR predictor of disability in RRMS subjects. Cognitive assessment of RRMS patients at five year follow-up is described, and brain atrophy rate was a significant predictor of overall cognitive performance, and more specifically, of performance in tests of memory. The work in this thesis has identified methods for sensitively measuring progressive brain atrophy in MS. It has shown that brain atrophy changes in early MS are related to early clinical evolution, providing complementary information to clinical assessment that could be utilised to monitor disease progression

Imaging of epileptic activity using EEG-correlated functional MRI.

This thesis describes the method of EEG-correlated fMRI and its application to patients with epilepsy. First, an introduction on MRI and functional imaging methods in the field of epilepsy is provided. Then, the present and future role of EEG-correlated fMRI in the investigation of the epilepsies is discussed. The fourth chapter reviews the important practicalities of EEG-correlated fMRI that were addressed in this project. These included patient safety, EEG quality and MRI artifacts during EEG-correlated fMRI. Technical solutions to enable safe, good quality EEG recordings inside the MR scanner are presented, including optimisation of the EEG recording techniques and algorithms for the on-line subtraction of pulse and image artifact. In chapter five, a study applying spike-triggered fMRI to patients with focal epilepsy (n = 24) is presented. Using statistical parametric mapping (SPM), cortical Blood Oxygen Level-Dependent (BOLD) activations corresponding to the presumed generators of the interictal epileptiform discharges (IED) were identified in twelve patients. The results were reproducible in repeated experiments in eight patients. In the remaining patients no significant activation (n = 10) was present or the activation did not correspond to the presumed epileptic focus (n = 2). The clinical implications of this finding are discussed. In a second study it was demonstrated that in selected patients, individual (as opposed to averaged) IED could also be associated with hemodynamic changes detectable with fMRI. Chapter six gives examples of combination of EEG-correlated fMRI with other modalities to obtain complementary information on interictal epileptiform activity and epileptic foci. One study compared spike-triggered fMRI activation maps with EEG source analysis based on 64-channel scalp EEG recordings of interictal spikes using co-registration of both modalities. In all but one patient, source analysis solutions were anatomically concordant with the BOLD activation. Further, the combination of spike- triggered fMRI with diffusion tensor and chemical shift imaging is demonstrated in a patient with localisation-related epilepsy. In chapter seven, applications of EEG-correlated fMRI in different areas of neuroscience are discussed. Finally, the initial imaging findings with the novel technique for the simultaneous and continuous acquisition of fMRI and EEG data are presented as an outlook to future applications of EEG-correlated fMRI. In conclusion, the technical problems of both EEG-triggered fMRI and simultaneous EEG-correlated fMRI are now largely solved. The method has proved useful to provide new insights into the generation of epileptiform activity and other pathological and physiological brain activity. Currently, its utility in clinical epileptology remains unknown

Statistical Techniques For Addressing The Clinico-Radiological Paradox In Multiple Sclerosis

Medical imaging technology has allowed for unparalleled insight into the structure and function of the human brain, giving clinicians powerful new tools for disease diagnosis and monitoring. Yet the complex and high-dimensional nature of imaging data makes computational analysis challenging. In multiple sclerosis (MS), this complexity is typically simplified by identifying regions of visible tissue damage and measuring spatial extent. However, many common radiological measures have been shown to be only weakly associated with clinical outcomes (a discovery that has been referred to as “the clinico-radiological paradox”). We attempt to bridge this gap by developing statistical methods capable of extracting clinically relevant information from MRI scans in MS. Here, we discuss three such techniques: a texture modeling approach to improve research on lesion dynamics; a biomarker detection algorithm to support diagnostic decision-making; and a flexible multi-modal group differences test to facilitate exploration of subtle disease processes. The performance of these methods is illustrated using simulated and real data, and the opportunities and obstacles for their clinical use are discussed

REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

Methods for Data Management in Multi-Centre MRI Studies and Applications to Traumatic Brain Injury

Neuroimaging studies are becoming increasingly bigger, and multi-centre collaborations to collect data under similar protocols, but different scanning sites, are now commonplace.However, with increasing sample size the complexity of databases and the entailed data management as well as computational burden are growing. This thesis aims to highlight and address challenges faced by large multi-centre magnetic resonance imaging(MRI) studies. The methods implemented are then applied to traumatic brain injury (TBI) data.Firstly, a pre-processing pipeline for both anatomical and diffusion MRI was proposed, that allows for a high throughput of MRI scans. After describing the choices for processing tools,the performance of the integrated quality assurance was assessed based on the results from a large multi-centre dataset for TBI. Secondly, the applicability of the pipelines for processing mild TBI (mTBI) data from three sites was shown in a case study. For this, volumetric and diffusion metrics in the acute phase are analysed for their prognostic potential. Further-more, the cohort was examined for longitudinal changes. Thirdly, independent scan-rescan datasets are examined to gain a better understanding of the degree of reproducibility which can be achieved in imaging studies. This involves analysing the robustness of brain parcellations based on structural or diffusion imaging. The effect of using different MRI scanners or imaging protocols was also assessed and discussed. Fourthly, sources of diffusion MRI variability and different approaches to cope with these are reviewed. Using this foundation,state-of-the art methods for diffusion MRI harmonisation were compared against each other using both a benchmark dataset and mTBI cohort. Lastly, a solution to localise brain lesions was proposed. Its implications for lesion analysis, are assessed in the light of an application to a more severe TBI patient cohort, imaged on two different scanners. Furthermore, a lesion matching algorithm was introduced to automatically examine lesion evolution with time post-injury. In summary, this thesis explored different options for MRI data analysis in the context of large multi-centre studies. Different approaches are studied and compared using a number of different MRI datasets, including scan-rescan data across different MRI scanners and imaging protocols. The potential of the optimised solutions was illustrated through applications to TBI data.CENTER-TB