Shanoir: Software as a Service Environment to Manage Population Imaging Research Repositories

International audienceSome of the major concerns of researchers and clinicians involved in popu- lation imaging experiments are on one hand, to manage the huge quantity and diversi- ty of produced data and, on the other hand, to be able to confront their experiments and the programs they develop with peers. In this context, we introduce Shanoir, a “Software as a Service” (SaaS) environment that offers cloud services for managing the information related to population imaging data production in the context of clini- cal neurosciences. We show how the produced images are accessible through the Sha- noir Data Management System, and we describe some of the data repositories that are hosted and managed by the Shanoir environment in different contexts

Modelisation statistique de formes en imagerie cerebrale

Jury : Patrick Bouthemy (President), Line Garnero et Gregoire Malandain (Rapporteurs), Christian Barillot, Michel Desvignes et Michel Dojat (Examinateurs).This thesis comes within the scope of statistical shape modeling in 3D cerebral imaging.In a first part, we propose a statistical shape model of cortical sulci. The model is built from a training population of sulci extracted from MRI volumes with a parametric representation. A coordinate system intrinsic to a sulcus shape is defined in order to align the training population, on which is then performed a principal components analysis. This statistical modeling is extended to a sulci graph in order to describe not only the morphological features of one sulcus, but also the relationships in terms of relative position and orientation between major sulci. The analysis we present is concerned with a reduced graph defined by a pair of sulci.In a second part, three applications are considered. On the one hand, we take part to an evaluation project of inter-subjects brain registration methods. When performed on local landmarks, the statistical analysis provides a similarity measure between registered shapes, and thus provides a comparison criterion between methods. On the other hand, we exploit the statistical knowledge acquired by the sulci modeling in the context of anatomical and functional atlases building. More precisely, we propose a fusion scheme, local and non-linear, to register inter-subjects functional data (MEG dipoles) toward a single coordinate system linked to the anatomical model of cortical sulci. Experimented on a database of 18 subjects, this method has been shown to reduce the observed inter-individual functional variability. Last, the methodology proposed to model cortical sulci shape is applied to functional borders shape delimiting low-order visual areas.Cette these traite de la modelisation statistique de formes en imageriecerebrale.Dans une premiere partie, nous proposons un modele statistique de la forme des sillons corticaux. Le modele est bati par apprentissage a partir de sillons extraits d'images IRM et dotes d'une representation parametrique. La definition d'un repere intrinseque a la forme sillon permet d'aligner l'ensemble des formes extraites et de construire une population d'apprentissage coherente sur laquelle appliquer une analyse en composantes principales afin de deriver le modele. Ce modele statistique est ensuite etendu a un graphe de sillons afin de decrire non plus seulement les caracteristiques morphologiques d'un sillon, mais aussi les relations de position et d'orientation entre sillons principaux.L'analyse presentee ici porte sur un sous-graphe defini par un couple de sillons. Dans une seconde partie, trois applications de la modelisation proposee sont envisagees. D'une part, nous l'utilisons dans un cadre d'evaluation de methodes de recalage global inter-sujets. Pratiquee sur des amers locaux, l'analyse statistique fournit un indicateur de la similarite des formes au sein des populations recalees, et produit un critere de comparaison entre les methodes. D'autre part, nous exploitons la connaissance statistique apportee par le modele sur les sillons dans le contexte de la construction d'atlas anatomiques et fonctionnels. Nous proposons une methode locale et non-lineaire de recalage inter-sujets de donnees fonctionnelles, exprimees sous forme de dipoles MEG (localisations d'activations fonctionnelles), base sur la modelisation des amers anatomiques que sont les sillons corticaux. Exprimentee sur une population de 18 sujets, cette methode s'est averee apte a reduire la variabilite fonctionnelle inter-individuelle observee. Enfin, nous appliquons la methodologie proposee dans le cas des sillons a la modelisation statistique de la forme de frontieres fonctionnelles delimitant des aires visuelles de bas-niveau

Resting-state ASL : Toward an optimal sequence duration

International audienceResting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily practice and academic research stay discreet compared to resting-state BOLD. However, by giving direct access to cerebral blood flow maps, rs-fASL leads to significant clinical subject scaled application as CBF can be considered as a biomarker in common neuropathology. Our work here focuses on the link between overall quality of rs-fASL and duration of acquisition. To this end, we consider subject self-Default Mode Network (DMN), and assess DMN quality depletion compared to a gold standard DMN depending on the duration of acquisition

Statistical modeling of pairs of sulci in the context of neuroimaging probabilistic atlas

International audienceIn the context of neuroimaging probabilistic atlases, we propose a statistical framework to model the inter-individual variability of pairs of sulci with respect to their relative position and orientation. The approach extends previous work [3], and relies on the statistical analysis of a training set. We first define an appropriate data representation, through an observation vector, in order to build a consistent training population, on which we then apply a normed principal components analysis (normed-PCA). Experiments have been performed on pairs of major sulci extracted from 18 MR images

Use Of A Probabilistic Shape Model For Non-Linear Registration Of 3D Scattered Data

In this paper we address the problem of registering 3D scattered data by the mean of a statistical shape model. This model is built from a training set on which a principal component analysis (PCA) is applied. A local system of reference is computed for each sample shape of the learning set, what enables to align the training set. PCA then reveals the main modes of deformation of the class of objects of interest. Furthermore, the deformation field obtained between a given shape and a reference shape is extended to a local neighborhood of these shapes by using an interpolation based on the thin-plate splines. It is then used to register objects associated with these shapes in a local and non-linear way. The data involved here are cerebral data both anatomical (cortical sulci) and functional (MEG dipoles)

Modélisation statistique de formes en imagerie cérébrale

RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis

journal articleDiffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity

Utilisation d'un modèle probabiliste de formes pour le recalage non-linéaire de données 3D éparses

National audienceNous nous attachons à recaler des données tridimensionnelles éparses via l'utilisation d'un modèle statistique de formes. Ce modèle est bâti à partir d'une population d'apprentissage, sur laquelle nous pratiquons ensuite une analyse en composantes principales (ACP). Nous calculons, pour chaque structure d'intérêt, un repère local propre à partir de ses axes d'inertie ; ce qui nous permet d'aligner, de fac¸on rigide, tous les exemplaires de l'ensemble d'apprentissage dans un référentiel local commun. Dans cet "espace local", les déformations d'une structure sont caractérisées par un vecteur de déplacement par rapport à une structure de référence (structure moyenne). Les composantes principales de la matrice de covariance des vecteurs de déplacement décrivent les modes principaux de déformation. D'autre part, le champ de déformation obtenu entre une forme donnée et une forme de référence est étendu à un voisinage local de la forme considérée en utilisant l'interpolation basée sur les thin-plate splines. Il peut alors être appliqué à tout objet qui aura été associé à cette forme, permettant ainsi le recalage local et non linéaire de l'objet. Les données traitées ici sont des données cérébrales. Il s'agit de structures anatomiques complexes : les sillons corticaux, et de données fonctionnelles particulières : les dipôles MEG

Towards a shape model of white matter fiber bundles using diffusion tensor MRI

White matter fiber bundles of the human brain form a spatial pattern defined by the anatomical and functional architecture. Human brain atlases provide names for individual tracts and document that these patterns are comparable across subjects. Tractography applied to the tensor field in diffusion tensor imaging (DTI) results in sets of streamlines which can be associated with major fiber tracts. Comparison of fiber tract properties across subjects requires comparison at corresponding anatomical locations. As an alternative to linear and nonlinear registration of DTI images and voxel-based analysis, we propose a novel methodology that models the shape of white matter tracts. A clustering uses similarity of adjacent curves and an iterative processing scheme to group sets of curves to bundles and to reject outliers. Unlike previous work which models fiber tracts as sets of curves centered around a spine, we extend the notion of bundling towards a more general representation of manifolds. We describe tracts, represented as sets of curves of similar shape, by a shape prototype swept along a space trajectory. This approach can naturally describe white matter structures observed either as bundles dispersing towards the cortex or tracts defined as dense patterns of parallel fibers forming manifolds. Curves are parameterized by arc-length and represented by intrinsic local shape properties (curvature and torsion). Feasibility is demonstrated by modeling the left and right cortico-spinal tracts and a part of the transversal callosal tract. 1

Non rigid registration in neuroimaging: from a retrospective evaluation of global approaches to new advances in local and hybrid methods

International audienceWithin the scope of three-dimensional brain imaging and brain functional mapping, we present a retrospective evaluation framework of inter-individual fusion scheme to register brain cortical anatomy and functional activations. The evaluation methodology relies on global and local criteria such as gray/white matter segmentation map overlap, correlation ratio of the mean Lvv intensities (i.e. 3D measure of the curvature of the cortex from the MRI volumes), distance and shape variation of cortical sulcal landmarks (segmented using the "active ribbon" approach). Visual and global measures seem to promote the non-linear global registration methods while local measures, based on sulci, did not show any significant differences between all global methods. As a perspective, new approaches are introduced to account for these local cortical deformations by means of either introducing sparse constraints in global registration methods or by introducing a local non-linear registration framework based on active shape models of local cortical features