Multivariate Autoregressive Model Constrained by Anatomical Connectivity to Reconstruct Focal Sources

International audienceIn this paper, we present a framework to reconstruct spatially localized sources from Magnetoencephalogra-phy (MEG)/Electroencephalography (EEG) using spatiotempo-ral constraint. The source dynamics are represented by a Mul-tivariate Autoregressive (MAR) model whose matrix elements are constrained by the anatomical connectivity obtained from diffusion Magnetic Resonance Imaging (dMRI). The framework assumes that the whole brain dynamic follows a constant MAR model in a time window of interest. The source activations and the MAR model parameters are estimated iteratively. We could confirm the accuracy of the framework using simulation experiments in both high and low noise levels. The proposed framework outperforms the two-stage approach

Détection des croisements de fibre en IRM de diffusion par décomposition de tenseur : Approche analytique

National audienceL'IRM de diffusion (IRMd) est l'unique modalité qui permet d'explorer les structures neuronales de la substance blanche in-vivo et de manière non-invasive. La diffusion a d'abord été modélisée par le modèle du tenseur de diffusion du second ordre (DTI). Toutefois, ce modèle trouve rapidement ses limites dans les zones, nombreuses, où les fibres de la matière blanche se croisent. Pour surmonter cette limite et reconstruire les croisements de fibres, différentes approches ont été proposées telles que: l'imagerie à résonance magnétique (IRM) à haute résolution angulaire (HARDI) et les tenseurs d'ordre supérieur (HOT) ; ces méthodes permettent de reconstruire des fonctions telle que la fonction de distribution d'orientation de fibre (FOD) dont les maxima s'alignent sur les orientations des fibres multiples. Dans ce travail, on se propose d'extraire les directions des fibres caractérisées par les maxima de la fonction FOD. Pour cela, une approche analytique de décomposition de tenseur symétrique a été implémentée et efficacement adaptée pour extraire les directions des fibres avec précision. Différents résultats obtenus sur des données synthétiques et réelles illustrent l'efficacité de la méthode

Use of Particle Swarm Optimization for ODF Maxima Extraction

International audienceFiber tracking is winning more and more interest in the neuroscience research field and clinical practice, for its ability in revealing the structural connectivity; the quality of the fiber tracking depends in great extent, on fiber directions extraction The PSO algorithm could give good approximation of these directions

Beyond Grid Portals and Towards SaaS A new access model for computational grids, in the MRI Brain context

International audienceAcigna-G is an ongoing research project to develop a new hybrid Grid SaaS architecture. CloudMRI is our proof-of-concept Acigna-G based SaaS Service for the Brain dMRI field. The main objective of such architecture is to provide local (Browser) and remote (Grid) intensive computational capabilities completly abstracted to the SaaS user. The result is a combination of an in Browser Rendering and Computation engine, interoperable REST-SOAP Grid Services, and interoperable web-grid authentication mechanisms. Such architecture can allow new types of SaaS Services, specifically for the dMRI Brain field

Crossing Fibers Detection with an Analytical High Order Tensor Decomposition

International audienceDiffusion magnetic resonance imaging (dMRI) is the only technique to probe in vivo and noninvasively the fiber structure of human brain white matter. Detecting the crossing of neuronal fibers remains an exciting challenge with an important impact in tractography. In this work, we tackle this challenging problem and propose an original and efficient technique to extract all crossing fibers from diffusion signals. To this end, we start by estimating, from the dMRI signal, the so-called Cartesian tensor fiber orientation distribution (CT-FOD) function, whose maxima correspond exactly to the orientations of the fibers. The fourth order symmetric positive definite tensor that represents the CT-FOD is then analytically decomposed via the application of a new theoretical approach and this decomposition is used to accurately extract all the fibers orientations. Our proposed high order tensor decomposition based approach is minimal and allows recovering the whole crossing fibers without any a priori information on the total number of fibers. Various experiments performed on noisy synthetic data, on phantom diffusion, data and on human brain data validate our approach and clearly demonstrate that it is efficient, robust to noise and performs favorably in terms of angular resolution and accuracy when compared to some classical and state-of-the-art approaches

A Hybrid SaaS/Grid Architecture for Diffusion MRI in Brain Imaging Field: SaaS as a New Access Model for Computation Grids

International audienceservice is built upon this architecture and is our proof-of-concept for the Diffusion Magnetic Resonance (dMRI) in Brain Imaging Field. The main objective of such architecture is to provide local (Browser) and remote (Grid) intensive computational capabilities, completely abstracted and offered through an intuitive interface to the SaaS user. The result is a combination of an in Browser Rendering and Computation engine, interoperable REST-SOAP Grid Services, and interoperable web-grid authentication mechanisms. Such architecture can allow new types of SaaS Services, specifically for the dMRI in Brain Imaging Field

ODF’s maxima extraction using Particle Swarm Optimization. Biomedical

International audiencePrevious works show that brain Diffusion MRI (dMRI) allow to give with more or less great precision the whitematter fiber structure and its possible intersection fibers. In this paper, we show that a Particle Swarm Optimization (PSO) based approach developed for brain dMRI allows to accurately recover the orientations of complex crossing fibers characterized as Orientation Distribution Function (ODF) maxima. Diffusion wasfirst modeled using the Diffusion Tensor Imaging (DTI), but this model has problems detecting crossing fibers and this has led to develop many other approaches to extract crossing fibers. Models have been proposed such as the High Order Tensor techniques and High Angular Resolution Diffusion Imaging (HARDI) from which we can reconstruct functions like ODF, whose maxima do correspond to the orientations of the multiple fibers. In this paper, we parametrize PSO to extract all the crossing fibers characterized as the maxima of the Orientation Distribution Function (PSO-ODF). Promising experimental results obtained with synthetic data illustrate the potential of the technique