Estimation de fonctions de densité des orientations asymétriques pour l'imagerie par résonance magnétique de diffusion

L'imagerie par résonance magnétique (IRM) de diffusion est une modalité d'acquisition permettant de mesurer le déplacement des molécules d'eau à l'intérieur d'un médium selon un ensemble de positions et de directions échantillonnées. Comme le déplacement des molécules d'eau à l'intérieur d'un élément de volume (voxel) est influencé par les configurations des axones le traversant, l'IRM de diffusion permet de sonder l'organisation structurelle du cerveau. Or, le signal de diffusion mesuré par l'IRM n'est pas aligné avec les orientations locales des axones. On doit donc d'abord le transformer en une image de fonctions de densité des orientations, décrivant la quantité apparente de fibres neuronales traversant chaque voxel selon une orientation donnée. La fonction de densité des orientations est habituellement modélisée par une fonction sphérique symétrique, assignant une même valeur scalaire à deux directions opposées sur la surface d'une sphère unitaire. Or, les trajectoires neuronales à l'intérieur d'un voxel ne sont pas nécessairement symétriques. En considérant le signal aux voxels voisins dans l'estimation de la fonction de densité des orientations pour un voxel donné, il est cependant possible d'estimer des fonctions de densité des orientations asymétriques, qui reproduisent plus fidèlement les configurations des fibres sous-jacentes. Ainsi, l'objectif de ce mémoire est de développer une nouvelle méthode de filtrage permettant de transformer une image de fonctions de densité des orientations symétriques en une image de fonctions de densités des orientations asymétriques, puis d'utiliser celle-ci afin d'étudier l'occurrence de configurations asymétriques à l'intérieur du cerveau acquis par IRM de diffusion. S'appuyant sur des mesures comme l'indice d'asymétrie, indiquant dans quelle mesure une fonction sphérique est asymétrique, et le nombre de directions de fibres, une mesure permettant la classification des fonctions sphériques asymétriques selon leur forme, des régions asymétriques sont identifiées. Ce mémoire montre que les configurations asymétriques surviennent dans au moins 40% des voxels de la matière blanche et 70% des voxels de la matière grise. Les fonctions de densité des orientations asymétriques estimées à partir de la méthode proposée capturent des trajectoires de fibres courbées, des terminaisons de trajectoires, des trajectoires en éventails, des embranchements et d'autres configurations complexes qui ne peuvent pas être représentées adéquatement en utilisant une fonction sphérique symétrique

Left-Invariant Diffusion on the Motion Group in terms of the Irreducible Representations of SO(3)

In this work we study the formulation of convection/diffusion equations on the 3D motion group SE(3) in terms of the irreducible representations of SO(3). Therefore, the left-invariant vector-fields on SE(3) are expressed as linear operators, that are differential forms in the translation coordinate and algebraic in the rotation. In the context of 3D image processing this approach avoids the explicit discretization of SO(3) or $S_2$, respectively. This is particular important for SO(3), where a direct discretization is infeasible due to the enormous memory consumption. We show two applications of the framework: one in the context of diffusion-weighted magnetic resonance imaging and one in the context of object detection

Statistical Learning Methods for Diffusion Magnetic Resonance Imaging

Diffusion Magnetic Resonance Imaging (dMRI) is a commonly used imaging technique to reveal white matter (WM) microstructure by probing the diffusion of water molecules. The diffusion of water molecules is constrained by the biological boundaries including nerves and tissues. Thus, quantifying the diffusion process is important to understand the WM microstructure. However, the development of efficient analytical methods for the reconstruction, lifespan structural connectome analysis, and surrogate variable analysis have fallenseriously behind the technological advances. This challenge motivates us to develop new statistical learning methods for dMRI. In the first project, we propose a two-stage sparse and adaptive smoothing model (TSASM) for two major image denoising tasks in neuroimaging data analysis, including image reconstruction from a series of noisy images within each subject and group analysis of images obtained from different subjects. Our TSASM consists of an initial smoothing stage of applying a penalized M-estimator and a refined smoothing stage of applying kernel-based smoothing methods. The key novelties of our TSASM are that it accounts for the sparse structure of imaging signals while preserving piecewise smooth regions with unknown edges. In the second project, we develop a scalable analytical method for mapping the lifespan human structural connectome. Specifically, we develop a novel lifespan population-based structural connectome (LPSC) framework that integrates fiber bundle and functional network information for hierarchically guiding the registration. Our LPSC is applicable to several neuroimaging studies of neuropsychiatric disorders as well as normal brain development. An improved understanding of human structural connectome has the potential to inspire new approaches to prevention, diagnosis, and treatment of many illnesses. In the third project, we propose an eigen-shrinkage projection (ESP) method to perform the surrogate variable analysis and solve the hidden confounder and harmonization problems in the neuroimaging studies. Our ESP can eliminate the signals from primary variable while preserving the eigenvalue-gap between hidden confounder and noises, which enables hidden confounders estimation from the projected data. We then investigate the statistical properties of the estimated hidden confounders and uncover the natural connection with ridge regression. Numerical experiments are used to illustrate the finite-sample performance.Doctor of Philosoph

Robust processing of diffusion weighted image data

The work presented in this thesis comprises a proposed robust diffusion weighted magnetic resonance imaging (DW-MRI) pipeline, each chapter detailing a step designed to ultimately transform raw DW-MRI data into segmented bundles of coherent fibre ready for more complex analysis or manipulation. In addition to this pipeline we will also demonstrate, where appropriate, ways in which each step could be optimized for the maxillofacial region, setting the groundwork for a wider maxillofacial modelling project intended to aid surgical planning. Our contribution begins with RESDORE, an algorithm designed to automatically identify corrupt DW-MRI signal elements. While slower than the closest alternative, RESDORE is also far more robust to localised changes in SNR and pervasive image corruptions. The second step in the pipeline concerns the retrieval of accurate fibre orientation distribution functions (fODFs) from the DW-MRI signal. Chapter 4 comprises a simulation study exploring the application of spherical deconvolution methods to `generic' fibre; finding that the commonly used constrained spherical harmonic deconvolution (CSHD) is extremely sensitive to calibration but, if handled correctly, might be able to resolve muscle fODFs in vivo. Building upon this information, Chapter 5 conducts further simulations and in vivo image experimentation demonstrating that this is indeed the case, allowing us to demonstrate, for the first time, anatomically plausible reconstructions of several maxillofacial muscles. To complete the proposed pipeline, Chapter 6 then introduces a method for segmenting whole volume streamline tractographies into anatomically valid bundles. In addition to providing an accurate segmentation, this shape-based method does not require computationally expensive inter-streamline comparisons employed by other approaches, allowing the algorithm to scale linearly with respect to the number of streamlines within the dataset. This is not often true for comparison based methods which in the best case scale in higher linear time but more often by O(N2) complexity

Characterising the structural brain changes in Huntington’s disease using translational neuroimaging

This thesis examined the macro-structural and micro-structural changes in Huntington’s disease (HD) in order to improve understanding of the temporal and spatial patterns of neurodegeneration, and the functional relevance of these changes. Translational techniques were employed using genetic mouse models of HD in combination with a patient cohort to examine grey and white matter changes with a particular focus on white matter microstructure. In the patient cohort, the cognitive profile was examined using a cognitive battery not before applied in HD. Specific deficits were found in set-shifting and flexibility, verbal reasoning, working memory and paired associate learning, along with subtle differences in response inhibition that were sensitive to disease burden. A composite cognitive score was produced to examine the relationship between cognitive function and brain structure. A multi-modal examination of white matter tract-specific microstructural measurements revealed abnormalities in the corpus callosum and cingulum bundle that were sensitive to disease burden (chapter 4). In chapter 5, multiple analysis techniques converged to reveal tissue macrostructure abnormalities that were also sensitive to disease burden in HD. Cortical changes were less consistent, and unlike the microstructure findings, white matter macrostructural abnormalities were not related to disease burden. In chapters 6 and 7, genetic mouse models of HD were used to examine changes across the disease course, and to pilot an interventional design. In vivo diffusion MRI and T2-weighted MRI sequences were acquired at 2 different time points in the HdhQ150 knock-in model of HD and imaging data is presented alongside behavioural results and immunohistochemistry. In chapter 7, an environmental modification regime was tested in the YAC128 mouse model using in vivo MRI. Environmental intervention reduced the degree of disease-related atrophy, altered tissue microstructure and improve motor but not cognitive performance in YAC128 mice

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf