A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures.

International audienceWe address the problem of quantitatively assessing myocardial function from tagged MRI sequences. We develop a two-step method comprising (i) a motion estimation step using a novel variational non-rigid registration technique based on generalized information measures, and (ii) a measurement step, yielding local and segmental deformation parameters over the whole myocardium. Experiments on healthy and pathological data demonstrate that this method delivers, within a reasonable computation time and in a fully unsupervised way, reliable measurements for normal subjects and quantitative pathology-specific information. Beyond cardiac MRI, this work redefines the foundations of variational non-rigid registration for information-theoretic similarity criteria with potential interest in multimodal medical imaging

Recalage variationnel non rigide statistique référencé région

Les mesures de similarité statistiques classiquement utilisées en recalage non rigide iconique exploitent des statistiques de luminance globales n'intégrant aucune information géométrique. Cette absence de prise en compte du contexte spatial peut conduire dans certains cas à un appariement imprécis voire incohérent de structures géométriques homologues, rendant ces critères mal adaptés à la segmentation par propagation d'atlas. Nous proposons de résoudre cette limitation en injectant dans la mesure de similarité un a priori spatial sous forme d'un modèle étiqueté de scène associé à l'image cible. Ce dernier induit un critère régionalisé fondé sur des densités de probabilité régionales, estimées via des noyaux de Parzen spécifiques sur chaque région du modèle. Nous formalisons ainsi les notions de rapport de corrélation et de f-information régionalisés, dont nous calculons les flots de gradient sur des espaces de transformations non paramétriques et paramétriques. L'application au modèle de scène de la transformation optimale inverse induit de facto une segmentation de l'image source. Nous illustrons les performances de cette approche pour la compensation de mouvements respiratoires et cardiaques complexes lors du transit d'un produit de contraste en IRM de perfusion

Recalage variationnel non rigide d'images par f-information exclusive

Nous nous intéressons à la mise en correspondance non rigide dense d'images dans des contextes monomodaux avec fortes variations photométriques/texturales ou multimodaux, et étudions à cette fin des critères de similarité statistiques fondés sur des mesures d'information généralisées au sein de la classe d'Ali-Silvey. Nous introduisons une nouvelle classe de fonctionnelles, dénommées f-informations exclusives, et développons un cadre variationnel générique bien-posé pour leur optimisation sur des espaces de déformations non paramétriques et paramétriques, généralisant les méthodes par information mutuelle. Cette approche est appliquée à l'alignement aveugle robuste de visages sous éclairement arbitraire et pour des déformations faciales complexes

Estimation paramétrique robuste à support optimal de la structure d'écoulements fluides en imagerie Météosat

Cet article présente une méthodologie d'analyse de la structure lagrangienne d'écoulements fluides dans des séquences d'images météorologiques multispectrales. Adoptant une approche multi-échelles, nous construisons tout d'abord, à partir du tenseur de structure de l'image, un estimateur non ponctuel robuste du champ d'orientation localement dominante, adapté aussi bien à un contexte monospectral que multispectral. Nous estimons ensuite la composante lagrangienne du flot en ajustant un modèle paramétrique vectoriel hiérarchique à ce champ d'orientation. Dans ce but, nous introduisons une approche variationnelle originale permettant l'optimisation conjointe des paramètres du modèle et de son support. La structure du champ de vecteurs résultant est finalement caractérisée au moyen d'outils classiques de géométrie différentielle. Cette méthodologie est appliquée à l'analyse de structures dépressionnaires en zone tempérée en imagerie Météosat

NCAM180 Regulates Ric8A Membrane Localization and Potentiates β-Adrenergic Response

Cooperation between receptors allows integrated intracellular signaling leading to appropriate physiological responses. The Neural Cell Adhesion Molecule (NCAM) has three main isoforms of 120, 140 and 180 kDa, with adhesive and signaling properties, but their respective functions remains to be fully identified. Here we show that the human NCAM180 intracellular domain is a novel interactor of the human guanosine exchange factor (GEF) Ric8A using the yeast two hybrid system and immunoprecipitation. Furthermore, NCAM, Ric8A and Gαs form a tripartite complex. Colocalization experiments by confocal microscopy revealed that human NCAM180 specifically induces the recruitment of Ric8A to the membrane. In addition, using an in vitro recombinant system, and in vivo by comparing NCAM knock-out mouse brain to NCAM heterozygous and wild type brains, we show that NCAM expression dose dependently regulates Ric8A redistribution in detergent resistent membrane microdomains (DRM). Previous studies have demonstrated essential roles for Ric8 in Gα protein activity at G protein coupled receptors (GPCR), during neurotransmitter release and for asymmetric cell division. We observed that inhibition of Ric8A by siRNA or its overexpression, decreases or increases respectively, cAMP production following β-adrenergic receptor stimulation. Furthermore, in human HEK293T recombinant cells, NCAM180 potentiates the Gαs coupled β-adrenergic receptor response, in a Ric8A dependent manner, whereas NCAM120 or NCAM140 do not. Finally, in mouse hippocampal neurons expressing endogenously NCAM, NCAM is required for the agonist isoproterenol to induce cAMP production, and this requirement depends on Ric8A. These data illustrate a functional crosstalk between a GPCR and an IgCAM in the nervous system

Understanding the Structure of Diffusive Scale-Spaces

. This paper investigates structural properties of diffusive scalespaces and develops a Riemannian description based on electromagnetic (EM) field theory. The generalized diffusion equation defining photometric transitions is interpreted as a Lorentz gauge condition expressing the trace Lorentzinvariance of an EM quadripotential with covariant scalar and contravariant vector components, respectively related to photometric and geometric image properties. This gauge condition determines EM quadrifield and quadricharge which satisfy Maxwell equations. Deriving their general expressions as functions of scale-space geometric or energetic features yields Lorentz-invariants which synthetize intrinsic multiscale image properties. Keywords : Multiscale analysis, geodesic flows, deformable manifolds, variational methods, gauge theory. 1 Introduction Anisotropic diffusion is an efficient nonlinear filtering technique for deriving deterministic multiscale image descriptions. Extensive studies ba..

Geometric Maxwell Equations and The Structure of Diffusive Scale-Spaces

In (linear or nonlinear) diffusive scale-space representations, local variations of the luminance field with respect to infinitesimal scale transitions are described via a first-order parabolic partial differential equation modeling a generalized diffusion process. A geometric characterization of the scale-space structure is then classically derived by analyzing the properties of the deformation flow induced by scale transitions along specific geometric structures embedded on the photometric surface. In particular, studying the simultaneous deformation of the dual families of curves consisting of isophotes and stream lines of the luminance field yields a Euclidean-invariant geometric description of generalized diffusion processes. In this paper, the generalized diffusion equation is interpreted within the framework of the relativistic electromagnetic (EM) theory as a Lorentz gauge condition expressing the trace-invariance of an EM quadripotential with covariant scalar and contravariant..

Controlled Anisotropic Diffusion

Anisotropic diffusion has been extensively used as an efficient nonlinear filtering technique for simultaneously performing contrast enhancement and noise reduction, and for deriving consistent scale-space image descriptions. In this paper, we present a general study of anisotropic diffusion schemes based on differential group-invariant representations of local image structure. We show that the local geometry (i.e. shape and scale) of the photometric surface is intrinsically specified by two dual families of curves, respectively consisting of isophotes and stream lines, which remain invariant under isometries in the image domain. Within this framework, anisotropic diffusive processes induce a deformation flow on the network of isophotes and stream lines. Deriving the general expression of this flow leads to identifying canonical forms for admissible conduction functions, that yield an optimal and stable preservation of significant image structures. Moreover, relating scale to direction..

Spatio-temporal registration of cardiac perfusion MRI exams using high-dimensional mutual information

International audienceCompensating for cardio-thoracic motion artifacts in contrast-enhanced cardiac perfusion MRI (p-MRI) sequences is a key issue for the quantitative assessment of myocardial ischæmia. The classical paradigm consists of registering each sequence frame on some reference using an intensity-based matching criterion. In this paper, we present a novel unsupervised method for the groupwise registration of cardiac p-MRI exams based on mutual information between high-dimensional feature distributions. Specifically, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched to a target feature distribution derived from a registered reference template. Using consistent kth nearest neighbors entropy estimators further enables the variational optimization of the model over finite- and infinite dimensional transform spaces. Experiments on simulated and natural datasets demonstrate its accuracy and relevance for the reliable assessment of regional perfusion