A Geometry-Based Particle Filtering Approach to White Matter Tractography

We introduce a fibre tractography framework based on a particle filter which estimates a local geometrical model of the underlying white matter tract, formulated as a `streamline flow' using generalized helicoids. The method is not dependent on the diffusion model, and is applicable to diffusion tensor (DT) data as well as to high angular resolution reconstructions. The geometrical model allows for a robust inference of local tract geometry, which, in the context of the causal filter estimation, guides tractography through regions with partial volume effects. We validate the method on synthetic data and present results on two types in vivo data: diffusion tensors and a spherical harmonic reconstruction of the fibre orientation distribution function (fODF)

New tractography methods based on parametric models of white matter fibre dispersion

Diffusion weighted magnetic resonance imaging (DW-MRI) is a powerful imaging technique that can probe the complex structure of the body, revealing structural trends which exist at scales far below the voxel resolution. Tractography utilises the information derived from DW-MRI to examine the structure of white matter. Using information derived from DW-MRI, tractography can estimate connectivity between distinct, functional cortical and sub-cortical regions of grey matter. Understanding how seperate functional regions of the brain are connected as part of a network is key to understanding how the brain works. Tractography has been used to deliniate many known white matter structures and has also revealed structures not fully understood from anatomy due to limitations of histological examination. However, there still remain many shortcomings of tractography, many anatomical features for which tractography algorithms are known to fail, which leads to discrepancies between known anatomy and tractography results. With the aim of approaching a complete picture of the human connectome via tractography, we seek to address the shortcomings in current tractography techniques by exploiting new advances in modelling techniques used in DW-MRI, which provide more accurate representation of underlying white matter anatomy. This thesis introduces a methodology for fully utilising new tissue models in DWMRI to improve tractography. It is known from histology that there are regions of white matter where fibres disperse or curve rapidly at length scales below the DW-MRI voxel resolution. One area where dispersion is particularly prominent is the corona radiata. New DW-MRI models capture dispersion utilising specialised parametric probability distributions. We present novel tractography algorithms utilising these parametric models of dispersion in tractography to improve connectivity estimation in areas of dispersing fibres. We first present an algorithm utilising the the new parametric models of dispersion for tractography in a simple Bayesian framework. We then present an extension to this algorithm which introduces a framework to pool neighbourhood information from multiple voxels in the neighbournhood surrounding the tract in order to better estimate connectivity, introducing the new concept of the neighbourhood-informed orientation distribution function (NI-ODF). Specifically, using neighbourhood exploration we address the ambiguity arising in ’fanning polarity’. In regions of dispersing fibres, the antipodal symmetry inherent in DW-MRI makes it impossible to resolve the polarity of a dispersing fibre configuration from a local voxel-wise model in isolation, by pooling information from neighbouring voxels, we show that this issue can be addressed. We evaluate the newly proposed tractography methods using synthetic phantoms simulating canonical fibre configurations and validate the ability to effectively navigate regions of dispersing fibres and resolve fanning polarity. We then validate that the algorithms perform effectively in real in vivo data, using DW-MRI data from 5 healthy subjects. We show that by utilising models of dispersion, we recover a wider range of connectivity compared to other standard algorithms when tracking through an area of the brain known to have significant white fibre dispersion - the corona radiata. We then examine the impact of the new algorithm on global connectivity estimates in the brain. We find that whole brain connectivity networks derived using the new tractography method feature strong connectivity between frontal lobe regions. This is in contrast to networks derived using competing tractography methods which do not account for sub-voxel fibre dispersion. We also compare thalamo-cortical connectivity estimated using the newly proposed tractography method and compare with a compteing tractography method, finding that the recovered connectivity profiles are largely similar, with some differences in thalamo-cortical connections to regions of the frontal lobe. The results suggest that fibre dispersion is an important structural feature to model in the basis of a tractography algorithm, as it has a strong effect on connectivity estimation

Diffusion directions imaging (high resolution reconstruction of white matter fascicles from low angular resolution diffusion MRI)

L'objectif de cette thèse est de fournir une chaine de traitement complète pour la reconstruction des faisceaux de la matière blanche à partir d'images pondérées en diffusion caractérisées par une faible résolution angulaire. Cela implique (i) d'inférer en chaque voxel un modèle de diffusion à partir des images de diffusion et (ii) d'accomplir une ''tractographie", i.e., la reconstruction des faisceaux à partir de ces modèles locaux. Notre contribution en modélisation de la diffusion est une nouvelle distribution statistique dont les propriétés sont étudiées en détail. Nous modélisons le phénomène de diffusion par un mélange de telles distributions incluant un outil de sélection de modèle destiné à estimer le nombre de composantes du mélange. Nous montrons que le modèle peut être correctement estimé à partir d'images de diffusion ''single-shell" à faible résolution angulaire et qu'il fournit des biomarqueurs spécifiques pour l'étude des tumeurs. Notre contribution en tractographie est un algorithme qui approxime la distribution des faisceaux émanant d'un voxel donné. Pour cela, nous élaborons un filtre particulaire mieux adapté aux distributions multi-modales que les filtres traditionnels. Pour démontrer l'applicabilité de nos outils en usage clinique, nous avons participé aux trois éditions du MICCAI DTI Tractography challenge visant à reconstruire le faisceau cortico-spinal à partir d'images de diffusion ''single-shell" à faibles résolutions angulaire et spatiale. Les résultats montrent que nos outils permettent de reconstruire toute l'étendue de ce faisceau.The objective of this thesis is to provide a complete pipeline that achieves an accurate reconstruction of the white matter fascicles using clinical diffusion images characterized by a low angular resolution. This involves (i) a diffusion model inferred in each voxel from the diffusion images and (ii) a tractography algorithm fed with these local models to perform the actual reconstruction of fascicles. Our contribution in diffusion modeling is a new statistical distribution, the properties of which are extensively studied. We model the diffusion as a mixture of such distributions, for which we design a model selection tool that estimates the number of mixture components. We show that the model can be accurately estimated from single shell low angular resolution diffusion images and that it provides specific biomarkers for studying tumors. Our contribution in tractography is an algorithm that approximates the distribution of fascicles emanating from a seed voxel. We achieve that by means of a particle filter better adapted to multi-modal distributions than the traditional filters. To demonstrate the clinical applicability of our tools, we participated to all three editions of the MICCAI DTI Tractography challenge aiming at reconstructing the cortico-spinal tract from single-shell low angular and low spatial resolution diffusion images. Results show that our pipeline provides a reconstruction of the full extent of the CST.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF