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

Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain

Morphometric characterisation of the cerebral cortex can provide information about patterns of brain development and ageing and may be relevant for diagnosis and estimation of the progression of diseases such as Alzheimer's, Huntington's, and schizophrenia. Therefore, understanding and describing the differences between populations in terms of structural volume, shape and thickness is of critical importance. Methodologically, due to data quality, presence of noise, PV effects, limited resolution and pathological variability, the automated, robust and time-consistent estimation of morphometric features is still an unsolved problem. This thesis focuses on the development of tools for robust cross-sectional and longitudinal morphometric characterisation of the human cerebral cortex. It describes techniques for tissue segmentation, structural and morphometric characterisation, cross-sectional and longitudinally cortical thickness estimation from serial MRI images in both adults and neonates. Two new probabilistic brain tissue segmentation techniques are introduced in order to accurately and robustly segment the brain of elderly and neonatal subjects, even in the presence of marked pathology. Two other algorithms based on the concept of multi-atlas segmentation propagation and fusion are also introduced in order to parcelate the brain into its multiple composing structures with the highest possible segmentation accuracy. Finally, we explore the use of the Khalimsky cubic complex framework for the extraction of topologically correct thickness measurements from probabilistic segmentations without explicit parametrisation of the edge. A longitudinal extension of this method is also proposed. The work presented in this thesis has been extensively validated on elderly and neonatal data from several scanners, sequences and protocols. The proposed algorithms have also been successfully applied to breast and heart MRI, neck and colon CT and also to small animal imaging. All the algorithms presented in this thesis are available as part of the open-source package NiftySeg

Automatic MRI segmentation of the developing neonatal brain

Detailed morphometric analysis of the neonatal brain is required to characterise normal brain development and investigate the neuroanatomical correlates of cognitive impairments. The segmentation of the brain in Magnetic Resonance Imaging (MRI) is a prerequisite to obtain quantitative measurements of regional brain structures. These measurements obtained at term-equivalent or early preterm age may lead to improved understanding of brain growth and may help evaluate long-term neurodevelopmental performance at an early stage. This thesis focuses on the development of an accurate segmentation algorithm for the neonatal brain MR images and its application in large cohorts of subjects. Neonatal brain segmentation is challenging due to the large anatomical variability as a result of the rapid brain development in the neonatal period. The lack of training data in the neonatal period, encoded in brain atlases, further hinders the development of automatic segmentation tools. A novel algorithm for the tissue segmentation of the neonatal brain is proposed. The algorithm is extended for the regional brain segmentation. This is the first segmentation method for the parcellation of the developing neonatal brain into multiple structures. A novel method is further proposed for the group-wise segmentation of the data that utilizes unlabelled data to complement the labelling information of brain atlases. Previous studies in the literature tended to overestimate the extent of the cortical region. A method based on the morphology of the cortex is introduced to correct for this over-segmentation. The segmentation method is applied on an extensive database of neonatal MR images. Regional volumetric, surface and diffusion tensor imaging measurements are derived from the early preterm period to term-equivalent age. These measurements allow characterisation of the regional brain development and the investigation of correlations with clinical factors. Finally, a spatio-temporal structural atlas is constructed for multiple regions of the neonatal brain.Open Acces

Visual attention models and arse representations for morphometrical image analysis

Abstract. Medical diagnosis, treatment, follow-up and research activities are nowadays strongly supported on different types of diagnostic images, whose main goal is to provide an useful exchange of medical knowledge. This multi-modal information needs to be processed in order to extract information exploitable within the context of a particular medical task. In despite of the relevance of these complementary sources of medical knowledge, medical images are rarely further processed in actual clinical practice, so the specialists take decisions only based in the raw data. A new trend in the development of medical image processing and analysis tools follows the idea of biologically-inspired methods, which resemble the performance of the human vision system. Visual attention models and sparse representations are examples of this tendency. Based on this, the aim of this thesis was the development of a set of computational methods for automatic morph metrical analysis, combining the relevant region extraction power of visual attention models with the incorporation of a priori information capabilities of sparse representations. The combination of these biologically inspired tools with common machine learning techniques allowed the identification of visual patterns relevant for pathology discrimination, improving the accuracy and interpretability of morph metric measures and comparisons. After extensive validations with different image data sets, the computational methods proposed in this thesis seems to be promising tools for the definition of anatomical biomarkers, based on visual pattern analysis, and suitable for patient's diagnosis, prognosis and follow-up.Las actividades de diagnóstico, tratamiento, seguimiento e investigación en medicina están actualmente soportadas en diferentes clases de imágenes diagnósticas, cuyo objetivo principal es el de proveer un intercambio efectivo de conocimiento médico. Esta información multimodal necesita ser procesada con el objetivo de extraer información aprovechable en el contexto de una tarea médica particular. A pesar de la relevancia de estas fuentes complementarias de información clínica, las imágenes médicas son raramente procesadas en la práctica clínica actual, de forma que los especialistas sólo toman decisiones basados en los datos crudos. Una nueva tendencia en el desarrollo de herramientas de análisis y procesamiento de imágenes médicas persigue la idea de métodos biológicamente inspirados, que se asemejan al sistema de visión humana. Son ejemplos de esta tendencia los modelos de atención visual y las representaciones escasas (sparse representations). Con base en esto, el objetivo de esta tesis fue el desarrollo de un conjunto de métodos computacionales para soportar automáticamente los análisis morfo métricos, combinando el poder de extracción de regiones relevantes de los modelos de atención visual junto con la capacidad de incorporación de información a priori de las representaciones escasas. La combinación de estos métodos biológicamente inspirados con técnicas de aprendizaje de maquina facilito la identificación de patrones visuales relevantes para discriminar patologías cerebrales, mejorando la precisión e interpretabilidad de las medidas y comparaciones morfo métricas. Después de extensivas validaciones con diferentes conjuntos de imágenes, los métodos computacionales propuestos en esta tesis se perfilan como herramientas prometedoras para la definición de biomarcadores anatómicos, basados en el análisis visual de patrones, y convenientes para el diagnóstico, pronóstico y seguimiento del paciente.Doctorad

Surface-Based tools for Characterizing the Human Brain Cortical Morphology

Tesis por compendio de publicacionesThe cortex of the human brain is highly convoluted. These characteristic convolutions present advantages over lissencephalic brains. For instance, gyrification allows an expansion of cortical surface area without significantly increasing the cranial volume, thus facilitating the pass of the head through the birth channel. Studying the human brain’s cortical morphology and the processes leading to the cortical folds has been critical for an increased understanding of the pathological processes driving psychiatric disorders such as schizophrenia, bipolar disorders, autism, or major depression. Furthermore, charting the normal developmental changes in cortical morphology during adolescence or aging can be of great importance for detecting deviances that may be precursors for pathology. However, the exact mechanisms that push cortical folding remain largely unknown. The accurate characterization of the neurodevelopment processes is challenging. Multiple mechanisms co-occur at a molecular or cellular level and can only be studied through the analysis of ex-vivo samples, usually of animal models. Magnetic Resonance Imaging can partially fill the breach, allowing the portrayal of the macroscopic processes surfacing on in-vivo samples. Different metrics have been defined to measure cortical structure to describe the brain’s morphological changes and infer the associated microstructural events. Metrics such as cortical thickness, surface area, or cortical volume help establish a relation between the measured voxels on a magnetic resonance image and the underlying biological processes. However, the existing methods present limitations or room for improvement. Methods extracting the lines representing the gyral and sulcal morphology tend to over- or underestimate the total length. These lines can provide important information about how sulcal and gyral regions function differently due to their distinctive ontogenesis. Nevertheless, some methods label every small fold on the cortical surface as a sulcal fundus, thus losing the perspective of lines that travel through the deeper zones of a sulcal basin. On the other hand, some methods are too restrictive, labeling sulcal fundi only for a bunch of primary folds. To overcome this issue, we have proposed a Laplacian-collapse-based algorithm that can delineate the lines traversing the top regions of the gyri and the fundi of the sulci avoiding anastomotic sulci. For this, the cortex, represented as a 3D surface, is segmented into gyral and sulcal surfaces attending to the curvature and depth at every point of the mesh. Each resulting surface is spatially filtered, smoothing the boundaries. Then, a Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the morphology of each structure. These thin curves are processed to detect where the extremities or endpoints lie. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the structure topology and connectivity between the endpoints. The assessment of the presented algorithm showed that the labeled sulcal lines were close to the proposed ground truth length values while crossing through the deeper (and more curved) regions. The tool also obtained reproducibility scores better or similar to those of previous algorithms. A second limitation of the existing metrics concerns the measurement of sulcal width. This metric, understood as the physical distance between the points on opposite sulcal banks, can come in handy in detecting cortical flattening or complementing the information provided by cortical thickness, gyrification index, or such features. Nevertheless, existing methods only provided averaged measurements for different predefined sulcal regions, greatly restricting the possibilities of sulcal width and ignoring the intra-region variability. Regarding this, we developed a method that estimates the distance from each sulcal point in the cortex to its corresponding opposite, thus providing a per-vertex map of the physical sulcal distances. For this, the cortical surface is sampled at different depth levels, detecting the points where the sulcal banks change. The points corresponding to each sulcal wall are matched with the closest point on a different one. The distance between those points is the sulcal width. The algorithm was validated against a simulated sulcus that resembles a simple fold. Then the tool was used on a real dataset and compared against two widely-used sulcal width estimation methods, averaging the proposed algorithm’s values into the same region definition those reference tools use. The resulting values were similar for the proposed and the reference methods, thus demonstrating the algorithm’s accuracy. Finally, both algorithms were tested on a real aging population dataset to prove the methods’ potential in a use-case scenario. The main idea was to elucidate fine-grained morphological changes in the human cortex with aging by conducting three analyses: a comparison of the age-dependencies of cortical thickness in gyral and sulcal lines, an analysis of how the sulcal and gyral length changes with age, and a vertex-wise study of sulcal width and cortical thickness. These analyses showed a general flattening of the cortex with aging, with interesting findings such as a differential age-dependency of thickness thinning in the sulcal and gyral regions. By demonstrating that our method can detect this difference, our results can pave the way for future in vivo studies focusing on macro- and microscopic changes specific to gyri or sulci. Our method can generate new brain-based biomarkers specific to sulci and gyri, and these can be used on large samples to establish normative models to which patients can be compared. In parallel, the vertex-wise analyses show that sulcal width is very sensitive to changes during aging, independent of cortical thickness. This corroborates the concept of sulcal width as a metric that explains, in the least, the unique variance of morphology not fully captured by existing metrics. Our method allows for sulcal width vertex-wise analyses that were not possible previously, potentially changing our understanding of how changes in sulcal width shape cortical morphology. In conclusion, this thesis presents two new tools, open source and publicly available, for estimating cortical surface-based morphometrics. The methods have been validated and assessed against existing algorithms. They have also been tested on a real dataset, providing new, exciting insights into cortical morphology and showing their potential for defining innovative biomarkers.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Juan Domingo Gispert López.- Secretario: Norberto Malpica González de Vega.- Vocal: Gemma Cristina Monté Rubi

Quantification of cortical folding using MR image data

The cerebral cortex is a thin layer of tissue lining the brain where neural circuits perform important high level functions including sensory perception, motor control and language processing. In the third trimester the fetal cortex folds rapidly from a smooth sheet into a highly convoluted arrangement of gyri and sulci. Premature birth is a high risk factor for poor neurodevelopmental outcome and has been associated with abnormal cortical development, however the nature of the disruption to developmental processes is not fully understood. Recent developments in magnetic resonance imaging have allowed the acquisition of high quality brain images of preterms and also fetuses in-utero. The aim of this thesis is to develop techniques which quantify folding from these images in order to better understand cortical development in these two populations. A framework is presented that quantifies global and regional folding using curvature-based measures. This methodology was applied to fetuses over a wide gestational age range (21.7 to 38.9 weeks) for a large number of subjects (N = 80) extending our understanding of how the cortex folds through this critical developmental period. The changing relationship between the folding measures and gestational age was modelled with a Gompertz function which allowed an accurate prediction of physiological age. A spectral-based method is outlined for constructing a spatio-temporal surface atlas (a sequence of mean cortical surface meshes for weekly intervals). A key advantage of this method is the ability to do group-wise atlasing without bias to the anatomy of an initial reference subject. Mean surface templates were constructed for both fetuses and preterms allowing a preliminary comparison of mean cortical shape over the postmenstrual age range 28-36 weeks. Displacement patterns were revealed which intensified with increasing prematurity, however more work is needed to evaluate the reliability of these findings.Open Acces

Development of an image processing pipeline for the study of corticol lesions in multiple sclerosis patients using ultra-high field MRI

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2019A esclerose múltipla é uma doença crónica e inflamatória do sistema nervoso central de alta prevalência nos dias de hoje. Durante anos, o foco da doença foi a patologia visível na matéria branca. Apesar dos primeiros estudos de patologia cortical em esclerose múltipla apontarem para a década de 60, foi apenas no início do novo século que o córtex passou a ser estudado como parte integral da doença. Desde então, estudos têm vindo a demonstrar que o comprometimento do córtex parece estar relacionado com danos cognitivos e físicos, frequentemente associados à doença. A necessidade de melhor compreender o impacto das lesões corticais no desenvolvimento da doença e na vida diária destes pacientes tem motivado o seu estudo, sendo a Ressonância Magnética (RM), em particular scanners de campo ultra-alto, a melhor ferramenta para as detetar e estudar. A melhoria da razão sinal-ruído e da resolução espacial dos scanners de RM de campo ultra-alto tem permitido o aumento da deteção de lesões corticais. Ainda assim, a sua sensibilidade continua a não ser ideal e a estar fortemente dependente do tipo de lesão cortical, do contraste de RM usado na sua deteção e da existência de ferramentas robustas que permitam a sua deteção de modo automático, mais eficiente e com menor espaço para erro. A falta de marcadores de imagem para a remielinização ou desmielinização parcial, tal como a ausência de diretrizes para a deteção destas lesões com campos de 7 (T)esla parece explicar a dificuldade em distinguir e identificar falsos positivos e as diferenças encontradas nas deteções realizadas por diferentes avaliadores. Uma desvantagem dos scanners de campo ultra-alto é o maior efeito de bias que, caso não seja removido aquando da aquisição de imagens, terá de ser removido na fase de processamento por softwares e algoritmos que não estão originalmente construídos para trabalhar com imagens de maior resolução e cuja prestação não está ainda bem explorada nestas condições. Estes desafios comprometem o potencial dos scanners de RM de campo ultra-alto para o estudo das lesões corticais na esclerose múltipla. Este projeto procura desenvolver uma pipeline semiautomática para o pré-processamento e processamento de imagens de RM de cariz estrutural de doentes com esclerose múltipla obtidas num scanner de campo ultra-alto. A pipeline é criada de modo gradual, recorrendo a análises visuais, ou de outro tipo, para confirmar a qualidade de cada passo antes de avançar para o seguinte, no pressuposto de que a qualidade dos softwares de imagem comercialmente disponíveis será menor ao utilizar imagens de maior resolução. A ocorrência de lesões corticais no córtex sensório-motor (SM1) é igualmente determinada e usada para validar a qualidade da pipeline. Doze doentes com esclerose múltipla na sua forma recidivante-remitente ou secundariamente progressiva e seis controlos foram incluídos neste projeto. Todas as permissões necessárias do comité local de ética, proteção de dados e da Danish Medicines Agency foram previamente obtidas. Os doentes foram estudados num scanner de RM de corpo inteiro da Philips, Achieva 7,0 T, dedicado a investigação. Os participantes foram observados usando quatro tipos distintos de contraste: magnetization prepared rapid acquisition by gradient echo (MPRAGE) a três dimensões (3D) com 0,65-mm de resolução isotrópica, 3D fluid attenuated inversion recovery (FLAIR) com 0,7-mm de resolução isotrópica, 3D T1-weighted (T1w) de resolução 0,85x0,85x1,0 mm3 e 3D T2-weighted Turbo Spin Echo (T2w-TSE) de 0,4-mm de resolução isotrópica. A vertente de pré-processamento da pipeline incluiu uma correção de bias e o co-registo de imagens. Para a correção de bias, o software SPM foi testado utilizando os parâmetros habituais e uma alteração dos parâmetros relativos à smoothness e regularização, como sugerido na literatura. O processo de co-registo seguiu o procedimento utilizado no processamento de imagens de doentes com esclerose múltipla de 3 T no Danish Research Centre for Magnetic Resonance (DRCMR), com alterações posteriormente adicionadas para melhorar a qualidade do alinhamento das imagens de cada indivíduo a 7 T. Após o pré-processamento, uma deteção de lesões corticais, seguida da sua segmentação, foi realizada manualmente utilizando as ferramentas do software FSL. A vertente de processamento da pipeline incluiu uma segmentação do cérebro, um registo das imagens dos doentes e a criação de superfícies corticais. A segmentação foi testada utilizando três diferentes ferramentas: o software SPM, uma toolbox do SPM, CAT, e a ferramenta de segmentação do FSL, FAST. A toolbox do SPM, DARTEL, foi usada no registo de imagens e o software FreeSurfer permitiu a criação de superfícies individuais e de grupo no último passo da pipeline. As máscaras com as lesões criadas após a segmentação manual de lesões seguiram um caminho semelhante de processamento de modo a permitir a sua correta sobreposição no respetivo volume, e, posteriormente, superfície, e a possibilidade de fazer análises individuais ou de grupo. Os resultados obtidos mostraram que os softwares para processamento de imagens de RM disponíveis apresentam, em geral, uma boa prestação e fornecem resultados de confiança. Ainda assim, a sua prestação pode ser otimizada incluindo procedimentos adicionais em cada passo ou por alteração das configurações originais dos softwares. A diminuição do parâmetro de largura à meia altura com um aumento do parâmetro de regularização na correção de bias com o SPM permitiu a criação de campos de bias mais fieis às imagens originais, consequentemente melhorando a sua correção e a diferenciação da matéria branca e matéria cinzenta nas imagens resultantes. A criação adicional de máscaras contendo apenas o cérebro e a utilização exclusiva de transformações de corpo rígido no co-registo de imagens permitiu a utilização de vários contrastes na tarefa de deteção de lesões, sem interferir com a sua localização ou morfologia. Na segmentação, a toolbox do SPM, CAT, mostrou melhorias na capacidade de separar as diferentes classes de tecidos com maior confiança e qualidade, particularmente nas regiões de contacto entre a matéria branca e cinzenta. Consequentemente, a qualidade do alinhamento das imagens dos diferentes doentes e a posterior criação de uma imagem média a partir de imagens individuais foi melhorada. O sucesso da pipeline permitiu a sobreposição das lesões corticais manualmente segmentadas nas superfícies individuais e/ou comuns criadas, onde foi descoberto que a maioria das lesões ocorreu no hemisfério direito, com sobreposições de lesões respetivas a diferentes doentes a ocorrer maioritariamente nos sulcos corticais, comparativamente aos giros. Porém, a segmentação de lesões demonstrou ser dispendiosa, dependente do avaliador e altamente influenciada por fatores inerentes ao avaliador, tal como o cansaço, nível de concentração ou de aborrecimento, e fatores externos, no qual se destacam a luminosidade do computador ou a luminosidade da sala onde a deteção foi feita. A feature do FreeSurfer para imagens de maior resolução não se mostrou fiável no tratamento dos dados de resolução isotrópica de 0,5-mm deste projeto, uma possível razão pela qual ainda se encontra em desenvolvimento. Apesar dos bons resultados obtidos, investigação adicional será necessária para melhor compreender a prestação destes e de outros softwares para imagem médica no processamento de imagens de RM de maior resolução, tal como a melhor maneira de tirar partido dos mesmos em estudos clínicos a 7 T. A extensão da pipeline a outros doentes com esclerose múltipla irá aumentar a amostra em estudo e permitir um estudo mais extensivo da patologia cortical e a compreensão do impacto de uma ou mais lesões localizadas na região SM1 na conectividade e integridade funcional da região cortical afetada.The importance of grey matter pathology to the understanding of multiple sclerosis has been acknowledged. However, the sensitivity to cortical lesions is limited when using conventional magnetic resonance imaging (MRI) systems. Ultra-high field (UHF) MRI systems have improved detection sensitivity but impose the additional challenge of a higher effect of bias to account for. Currently, image processing tools are not designed for higher resolution data and the performance of common software packages under these conditions has not been properly explored. These challenges have impaired the potential of UHF-MRI to study cortical lesions in multiple sclerosis. This project aims at developing a semi-automated pipeline for the pre-processing and processing of structural UHF-MRI data of multiple sclerosis patients. The pipeline is built in a step-by-step fashion, making use of visual assessments and other analyses to confirm the quality of each step before advancing to the next, under the assumption that the performance of common imaging software packages will be poorer when using higher resolution data. The occurrence of cortical lesions within the primary sensory-motor cortex (SM1) is also determined and used to validate the quality of the pipeline. Twelve patients with relapsing-remitting multiple sclerosis or secondary progressive multiple sclerosis and six healthy age-matched controls were included in this project. All relevant permissions from the local ethics committee and data protection had been obtained beforehand. All participants were studied with whole-brain ultra-high field MRI at 7 Tesla (T), using a research-only 7 T Achieva MR system. The participants were scanned using four different MRI modalities, namely 3-dimensional (3D) magnetization prepared rapid acquisition by gradient echo (MPRAGE) at 0.65-mm isotropic resolution, 3D fluid attenuated inversion recovery (FLAIR) at 0.7-mm isotropic resolution, 3D T1-weighted (T1w) of 0.85x0.85x1.0 mm3 reconstructed resolution and 3D T2-weighted Turbo Spin Echo (T2w-TSE) at 0.4-mm isotropic reconstructed resolution. The pre-processing pipeline included a bias correction and a coregistration step. For the bias correction, SPM was tested using its default parameters and an alternative configuration that altered the smoothness and regularization parameters. The coregistration followed an approach used in the processing of multiple sclerosis data at 3 T, with changes added to improve the quality of the within-subject alignment at 7 T. After the data pre-processing, manual detection and segmentation of cortical lesions was performed using FSLeyes. The processing pipeline included brain segmentation, subject registration and cortical surface creation. Brain segmentation was tested with SPM, with SPM’s toolbox, CAT, and with FSL’s segmentation tool, FAST. SPM’s DARTEL tool was used for subject registration and FreeSurfer allowed the creation of individual and an average cortical surface. The lesion masks created after the manual segmentation task followed a similar processing route to allow their overlay on the respective brain volumes and, posteriorly, surfaces, and the possibility of individual and group analyses. Results showed that the currently available MRI image processing tools present overall good performance and reliability in the processing of higher resolution data of multiple sclerosis patients. Still, the quality of the outcomes can be optimized by including additional steps or changes to the original software configurations. Modifying SPM’s smoothness and regularization parameters for the estimation of bias minimized its effect in the data, allowing a better differentiation between grey matter and white matter. Removing the skull whilst keeping the coregistration to rigid body transformations allowed the use of several contrasts in the lesion detection task without interfering with the lesions’ morphology and topography. Brain segmentation using CAT showed more stability across the dataset, improving the quality of the subsequent subject registration and consequently of the average brain created. The success of the pipeline led to the possibility of overlaying the manually segmented lesions on the individual and group surfaces where it was found that the majority of lesions occurred on the right hemisphere and that lesion overlaps were more common in cortical sulci. Despite the results obtained, further research is needed to understand the performance of other software packages in the processing of higher resolution MRI data and how to fully exploit these tools in the study of clinical data at 7 T

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI