Méthodes de Traitement d'Image Appliquées au Problème Inverse en Magnéto-Electro-Encéphalographie

This PhD work deals with forward and inverse problem in magnetoencephalography (MEG) and electroencephalography (EEG). Three topics are addressed. The forward problem is issued by the boundary element method (BEM). A new formulation, called symmetric formulation, is proposed. This new formulation is then applied to the electrical impedance tomography (EIT) for which two conductivity estimation algorithms are proposed. The inverse problem is addressed in the imaging approach framework. Image regularization techniques by means of diffusion processes are transposed to the inverse problem in order to constrain the reconstruction of distributed sources. Several algorithms are proposed, one of them computes the minimal total variation solution.Ce travail de Thèse traite des problèmes directs et inverses de la magnétoencéphalographie (MEG) et de l'électroencéphalographie (EEG). Trois thématiques y sont abordées. Le problème direct est traité à l'aide des méthodes d'éléments frontière. Une nouvelle formulation, dite formulation symétrique, est proposée. Cette nouvelle formulation est ensuite appliquée au problème de la tomographie par impédance électrique pour lequel deux algorithmes d'estimation de conductivité sont proposés. Le problème inverse est traité dans le cadre des méthodes image. Des techniques de régularisation d'image par processus de diffusion sont transposées au problème inverse pour contraindre la reconstruction de sources distribuées. Plusieurs algorithmes sont proposés dont un calculant la solution inverse de variation totale minimale

Méthodes de traitement d'image appliquées au problème inverse en magnéto-électro-encéphalographie

MARNE-LA-VALLEE-ENPC-BIBL. (774682303) / SudocSudocFranceF

Imaging Methods for MEG/EEG Inverse Problem

Recovering electrical activity of the brain from MEG/EEG measurements is known as the MEEG inverse problem. It is an ill-posed problem in several senses. One is that there is further less data observed than data to recover. One way to address this issue is to search for regular solutions. We present here a framework for applying image processing filtering techniques to the MEEG inverse problem. Exprimentations are presented on synthetic dara and validation is carried out on one real MEG data set

EOS developments

CERN has been developing and operating EOS as a disk storage solution successfully for over 6 years. The CERN deployment provides 135 PB and stores 1.2 billion replicas distributed over two computer centres. Deployment includes four LHC instances, a shared instance for smaller experiments and since last year an instance for individual user data as well. The user instance represents the backbone of the CERNBOX service for file sharing. New use cases like synchronisation and sharing, the planned migration to reduce AFS usage at CERN and the continuous growth has brought EOS to new challenges. Recent developments include the integration and evaluation of various technologies to do the transition from a single active in-memory namespace to a scale-out implementation distributed over many meta-data servers. The new architecture aims to separate the data from the application logic and user interface code, thus providing flexibility and scalability to the namespace component. Another important goal is to provide EOS as a CERN-wide mounted filesystem with strong authentication making it a single storage repository accessible via various services and front- ends (/eos initiative). This required new developments in the security infrastructure of the EOS FUSE implementation. Furthermore, there were a series of improvements targeting the end-user experience like tighter consistency and latency optimisations. In collaboration with Seagate as Openlab partner, EOS has a complete integration of OpenKinetic object drive cluster as a high-throughput, high-availability, low-cost storage solution. This contribution will discuss these three main development projects and present new performance metrics

apport de rechercheVariational, Geometric and Statistical Methods for Modeling Brain Anatomy and Function

Abstract: We survey the recent activities of the Odyssée Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical MR images. Anatomical connectivity can be extracted from Diffusion Tensor Magnetic Resonance Images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter as geodesics in some Riemannian space. We then go to the statistical modeling of fMRI signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part which we then use to perform clustering of voxels in a way that is inspired by the theory of Support Vector Machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in th

Variational, Geometric and Statistical Methods for Modeling Brain Anatomy and Function

We survey the recent activities of the Odyss ee Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical MR images. Anatomical connectivity can be extracted from Diffusion Tensor Magnetic Resonance Images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter as geodesics in some Riemannian space. We then go to the statistical modeling of fMRI signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part which we then use to perform clustering of voxels in a way that is inspired by the theory of Support Vector Machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in the framework of image statistics and Partial Differential Equations with an eye on registering fMRI to the anatomy. The paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases