MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING

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

RECOVERING LOCAL NEURAL TRACT DIRECTIONS AND RECONSTRUCTING NEURAL PATHWAYS IN HIGH ANGULAR RESOLUTION DIFFUSION MRI

Magnetic resonance imaging (MRI) is an imaging technique to visualize internal structures of the body. Diffusion MRI is an MRI modality that measures overall diffusion effect of molecules in vivo and non-invasively. Diffusion tensor imaging (DTI) is an extended technique of diffusion MRI. The major application of DTI is to measure the location, orientation and anisotropy of fiber tracts in white matter. It enables non-invasive investigation of major neural pathways of human brain, namely tractography. As spatial resolution of MRI is limited, it is possible that there are multiple fiber bundles within the same voxel. However, diffusion tensor model is only capable of resolving a single direction. The goal of this dissertation is to investigate complex anatomical structures using high angular resolution diffusion imaging (HARDI) data without any assumption on the parameters. The dissertation starts with a study of the noise distribution of truncated MRI data. The noise is often not an issue in diffusion tensor model. However, in HARDI studies, with many more gradient directions being scanned, the number of repetitions of each gradient direction is often small to restrict total acquisition time, making signal-to-noise ratio (SNR) lower. Fitting complex diffusion models to data with reduced SNR is a major interest of this study. We focus on fitting diffusion models to data using maximum likelihood estimation (MLE) method, in which the noise distribution is used to maximize the likelihood. In addition to the parameters being estimated, we use likelihood values for model selection when multiple models are fit to the same data. The advantage of carrying out model selection after fitting the models is that both the quality of data and the quality of fitting results are taken into account. When it comes to tractography, we extend streamline method by using covariance of the estimated parameters to generate probabilistic tracts according to the uncertainty of local tract orientations

Improving the Tractography Pipeline: on Evaluation, Segmentation, and Visualization

Recent advances in tractography allow for connectomes to be constructed in vivo. These have applications for example in brain tumor surgery and understanding of brain development and diseases. The large size of the data produced by these methods lead to a variety problems, including how to evaluate tractography outputs, development of faster processing algorithms for tractography and clustering, and the development of advanced visualization methods for verification and exploration. This thesis presents several advances in these fields. First, an evaluation is presented for the robustness to noise of multiple commonly used tractography algorithms. It employs a Monte–Carlo simulation of measurement noise on a constructed ground truth dataset. As a result of this evaluation, evidence for obustness of global tractography is found, and algorithmic sources of uncertainty are identified. The second contribution is a fast clustering algorithm for tractography data based on k–means and vector fields for representing the flow of each cluster. It is demonstrated that this algorithm can handle large tractography datasets due to its linear time and memory complexity, and that it can effectively integrate interrupted fibers that would be rejected as outliers by other algorithms. Furthermore, a visualization for the exploration of structural connectomes is presented. It uses illustrative rendering techniques for efficient presentation of connecting fiber bundles in context in anatomical space. Visual hints are employed to improve the perception of spatial relations. Finally, a visualization method with application to exploration and verification of probabilistic tractography is presented, which improves on the previously presented Fiber Stippling technique. It is demonstrated that the method is able to show multiple overlapping tracts in context, and correctly present crossing fiber configurations

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation–diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo‐times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation–diffusion distributions where contributions from different sub‐voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre‐specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation‐specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre‐tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways

Representation Learning With Convolutional Neural Networks

Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data. In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain fiber tracts. First, we propose a topic-based word representation learning approach for text classification. The proposed approach takes global semantic relationship between words over the whole corpus into consideration and encodes the relationships into distributed vector representations with continuous Skip-gram model. The learned representations which capture a large number of precise syntactic and semantic word relationships are taken as input of Convolution Neural Networks for classification. Our experimental results show the effectiveness of the proposed method on indexing of biomedical articles, behavior code annotation of clinical text fragments, and classification of news groups. Second, we present a 3D polygon shape representation learning framework for shape segmentation. We propose Directionally Convolutional Network (DCN) that extends convolution operations from images to the polygon mesh surface with rotation-invariant property. Based on the proposed DCN, we learn effective shape representations from raw geometric features and then classify each face of a given polygon into predefined semantic parts. Through extensive experiments, we demonstrate that our framework outperforms the current state-of-the-arts. Third, we propose to learn effective and meaningful representations for brain fiber tracts using deep learning frameworks. We handle the highly unbalanced dataset by introducing asymmetrical loss function for easily classified samples and hard classified ones. The training loss avoids to be dominated by the easy samples and the training step is more efficient. In addition, we learn more effective and meaningful representations by introducing deeper network and metric learning approaches. Furthermore, we propose to improve the interpretability of our framework by inducing attention mechanism. Our experimental results show that our proposed framework outperforms current golden standard significantly on the real-world dataset

Unsupervised deep learning of human brain diffusion magnetic resonance imaging tractography data

L'imagerie par résonance magnétique de diffusion est une technique non invasive permettant de connaître la microstructure organisationnelle des tissus biologiques. Les méthodes computationnelles qui exploitent la préférence orientationnelle de la diffusion dans des structures restreintes pour révéler les voies axonales de la matière blanche du cerveau sont appelées tractographie. Ces dernières années, diverses méthodes de tractographie ont été utilisées avec succès pour découvrir l'architecture de la matière blanche du cerveau. Pourtant, ces techniques de reconstruction souffrent d'un certain nombre de défauts dérivés d'ambiguïtés fondamentales liées à l'information orientationnelle. Cela a des conséquences dramatiques, puisque les cartes de connectivité de la matière blanche basées sur la tractographie sont dominées par des faux positifs. Ainsi, la grande proportion de voies invalides récupérées demeure un des principaux défis à résoudre par la tractographie pour obtenir une description anatomique fiable de la matière blanche. Des approches méthodologiques innovantes sont nécessaires pour aider à résoudre ces questions. Les progrès récents en termes de puissance de calcul et de disponibilité des données ont rendu possible l'application réussie des approches modernes d'apprentissage automatique à une variété de problèmes, y compris les tâches de vision par ordinateur et d'analyse d'images. Ces méthodes modélisent et trouvent les motifs sous-jacents dans les données, et permettent de faire des prédictions sur de nouvelles données. De même, elles peuvent permettre d'obtenir des représentations compactes des caractéristiques intrinsèques des données d'intérêt. Les approches modernes basées sur les données, regroupées sous la famille des méthodes d'apprentissage profond, sont adoptées pour résoudre des tâches d'analyse de données d'imagerie médicale, y compris la tractographie. Dans ce contexte, les méthodes deviennent moins dépendantes des contraintes imposées par les approches classiques utilisées en tractographie. Par conséquent, les méthodes inspirées de l'apprentissage profond conviennent au changement de paradigme requis, et peuvent ouvrir de nouvelles possibilités de modélisation, en améliorant ainsi l'état de l'art en tractographie. Dans cette thèse, un nouveau paradigme basé sur les techniques d'apprentissage de représentation est proposé pour générer et analyser des données de tractographie. En exploitant les architectures d'autoencodeurs, ce travail tente d'explorer leur capacité à trouver un code optimal pour représenter les caractéristiques des fibres de la matière blanche. Les contributions proposées exploitent ces représentations pour une variété de tâches liées à la tractographie, y compris (i) le filtrage et (ii) le regroupement efficace sur les résultats générés par d'autres méthodes, ainsi que (iii) la reconstruction proprement dite des fibres de la matière blanche en utilisant une méthode générative. Ainsi, les méthodes issues de cette thèse ont été nommées (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), et (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectivement. Les performances des méthodes proposées sont évaluées par rapport aux méthodes de l'état de l'art sur des données de diffusion synthétiques et des données de cerveaux humains chez l'adulte sain in vivo. Les résultats montrent que (i) la méthode de filtrage proposée offre une sensibilité et spécificité supérieures par rapport à d'autres méthodes de l'état de l'art; (ii) le regroupement des tractes dans des faisceaux est fait de manière consistante; et (iii) l'approche générative échantillonnant des tractes comble mieux l'espace de la matière blanche dans des régions difficiles à reconstruire. Enfin, cette thèse révèle les possibilités des autoencodeurs pour l'analyse des données des fibres de la matière blanche, et ouvre la voie à fournir des données de tractographie plus fiables.Abstract : Diffusion magnetic resonance imaging is a non-invasive technique providing insights into the organizational microstructure of biological tissues. The computational methods that exploit the orientational preference of the diffusion in restricted structures to reveal the brain's white matter axonal pathways are called tractography. In recent years, a variety of tractography methods have been successfully used to uncover the brain's white matter architecture. Yet, these reconstruction techniques suffer from a number of shortcomings derived from fundamental ambiguities inherent to the orientation information. This has dramatic consequences, since current tractography-based white matter connectivity maps are dominated by false positive connections. Thus, the large proportion of invalid pathways recovered remains one of the main challenges to be solved by tractography to obtain a reliable anatomical description of the white matter. Methodological innovative approaches are required to help solving these questions. Recent advances in computational power and data availability have made it possible to successfully apply modern machine learning approaches to a variety of problems, including computer vision and image analysis tasks. These methods model and learn the underlying patterns in the data, and allow making accurate predictions on new data. Similarly, they may enable to obtain compact representations of the intrinsic features of the data of interest. Modern data-driven approaches, grouped under the family of deep learning methods, are being adopted to solve medical imaging data analysis tasks, including tractography. In this context, the proposed methods are less dependent on the constraints imposed by current tractography approaches. Hence, deep learning-inspired methods are suit for the required paradigm shift, may open new modeling possibilities, and thus improve the state of the art in tractography. In this thesis, a new paradigm based on representation learning techniques is proposed to generate and to analyze tractography data. By harnessing autoencoder architectures, this work explores their ability to find an optimal code to represent the features of the white matter fiber pathways. The contributions exploit such representations for a variety of tractography-related tasks, including efficient (i) filtering and (ii) clustering on results generated by other methods, and (iii) the white matter pathway reconstruction itself using a generative method. The methods issued from this thesis have been named (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), and (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectively. The proposed methods' performance is assessed against current state-of-the-art methods on synthetic data and healthy adult human brain in vivo data. Results show that the (i) introduced filtering method has superior sensitivity and specificity over other state-of-the-art methods; (ii) the clustering method groups streamlines into anatomically coherent bundles with a high degree of consistency; and (iii) the generative streamline sampling technique successfully improves the white matter coverage in hard-to-track bundles. In summary, this thesis unlocks the potential of deep autoencoder-based models for white matter data analysis, and paves the way towards delivering more reliable tractography data

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018