4-D Gradient Vector Flow : segmentation par surface active pour images multi-composantes

National audienceDans cet article, nous généralisons le flux de vecteurs gradients à la segmentation par surface active d'images 3-D à valeurs vectorielles. Nous basons notre méthode sur la définition d'un tenseur de structure multi-composantes pondéré exploitant l'intégralité de l'information de l'image pour réduire la sensibilité au bruit et améliorer la précision du modèle. Appliquée à la segmentation de volumes biologiques en imagerie par tomographie d'émission de positrons (TEP) dynamique, nous validons notre méthode sur des simulations Monte Carlo réalistes d'images TEP de fantômes numériques

Variational segmentation of vector-valued images with gradient vector flow

International audienceIn this paper, we extend the gradient vector flow field for robust variational segmentation of vector-valued images. Rather than using scalar edge information, we define a vectorial edge map derived from a weighted local structure tensor of the image that enables the diffusion of the gradient vectors in accurate directions through the 4DGVF equation. To reduce the contribution of noise in the structure tensor, image channels are weighted according to a blind estimator of contrast. The method is applied to biological volume delineation in dynamic PET imaging, and validated on realistic Monte Carlo simulations of numerical phantoms as well as on real images

Reconstruction multiclasse pour l'imagerie TEP 3-photons

This contribution addresses the problem of image reconstruction of radioactivity distribution for which the available information arises from several classes of data, each associated with a specific combination of detections. We introduce a theoretical framework to measure the amount of information brought by each class and we develop an iterative algorithm dedicated to multi-class reconstruction based on maximum likelihood.We apply our approach to the XEMIS2 camera, a preclinical prototype of a Compton telescope dedicated to 3-photon PET imaging for which four distinct classes of partial detections coexist with the full detection class.Based on Monte Carlo simulations, we present the first elements of our model.Comment: in French language, GRETSI'23, Groupe de Recherche et d'Etudes de Traitement du Signal et des Images, Aug 2023, Grenoble, Franc

Segmentation of Dynamic PET Images with Kinetic Spectral Clustering

International audienceSegmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks

3D+t segmentation of PET images using spectral clustering

International audienceSegmentation of dynamic PET images is often needed to extract the time activity curve (TAC) of regions. While clustering methods have been proposed to segment the PET sequence, they are generally either sensitive to initial conditions or favor convex shaped clusters. Recently, we have proposed a deterministic and automatic spectral clustering method (AD-KSC) of PET images. It has the advantage of handling clusters with arbitrary shape in the space in which they are identified. While improved results were obtained with AD-KSC compared to other methods, its use for clinical applications is constrained to 2D+t PET data due to its computational complexity. In this paper, we propose an extension of AD-KSC to make it applicable to 3D+t PET data. First, a preprocessing step based on a recursive principle component analysis and a Global K-means approach is used to generate many small seed clusters. AD-KSC is then applied on the generated clusters to obtain the final partition of the data. We validated the method with GATE Monte Carlo simulations of Zubal head phantom. The proposed approach improved the region of interest (ROI) definition and outperformed the K-means algorithm

Unmixing dynamic PET images with variable specific binding kinetics

To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal component analysis (PCA), independent component analysis (ICA), factor analysis and nonnegative matrix factorization (NMF). Nevertheless, these conventional approaches neglect any possible nonlinear variations in the time activity curves describing the kinetic behavior of tissues with specific binding, which limits their ability to recover a reliable, understandable and interpretable description of the data. This paper proposes an alternative analysis paradigm that accounts for spatial fluctuations in the exchange rate of the tracer between a free compartment and a specifically bound ligand compartment. The method relies on the concept of linear unmixing, usually applied on the hyperspectral domain, which combines NMF with a sum-to-one constraint that ensures an exhaustive description of the mixtures. The spatial variability of the signature corresponding to the specific binding tissue is explicitly modeled through a perturbed component. The performance of the method is assessed on both synthetic and real data and is shown to compete favorably when compared to other conventional analysis methods

Dissolved noble gases and stable isotopes as tracers of preferential fluid flow along faults in the Lower Rhine Embayment, Germany

Groundwater in shallow unconsolidated sedimentary aquifers close to the Bornheim fault in the Lower Rhine Embayment (LRE), Germany, has relatively low δ2H and δ18O values in comparison to regional modern groundwater recharge, and 4He concentrations up to 1.7 × 10−4 cm3 (STP) g–1 ± 2.2 % which is approximately four orders of magnitude higher than expected due to solubility equilibrium with the atmosphere. Groundwater age dating based on estimated in situ production and terrigenic flux of helium provides a groundwater residence time of ∼107 years. Although fluid exchange between the deep basal aquifer system and the upper aquifer layers is generally impeded by confining clay layers and lignite, this study’s geochemical data suggest, for the first time, that deep circulating fluids penetrate shallow aquifers in the locality of fault zones, implying  that sub-vertical fluid flow occurs along faults in the LRE. However, large hydraulic-head gradients observed across many faults suggest that they act as barriers to lateral groundwater flow. Therefore, the geochemical data reported here also substantiate a conduit-barrier model of fault-zone hydrogeology in unconsolidated sedimentary deposits, as well as corroborating the concept that faults in unconsolidated aquifer systems can act as loci for hydraulic connectivity between deep and shallow aquifers. The implications of fluid flow along faults in sedimentary basins worldwide are far reaching and of particular concern for carbon capture and storage (CCS) programmes, impacts of deep shale gas recovery for shallow groundwater aquifers, and nuclear waste storage sites where fault zones could act as potential leakage pathways for hazardous fluids

Towards Quantum Repeaters with Solid-State Qubits: Spin-Photon Entanglement Generation using Self-Assembled Quantum Dots

In this chapter we review the use of spins in optically-active InAs quantum dots as the key physical building block for constructing a quantum repeater, with a particular focus on recent results demonstrating entanglement between a quantum memory (electron spin qubit) and a flying qubit (polarization- or frequency-encoded photonic qubit). This is a first step towards demonstrating entanglement between distant quantum memories (realized with quantum dots), which in turn is a milestone in the roadmap for building a functional quantum repeater. We also place this experimental work in context by providing an overview of quantum repeaters, their potential uses, and the challenges in implementing them.Comment: 51 pages. Expanded version of a chapter to appear in "Engineering the Atom-Photon Interaction" (Springer-Verlag, 2015; eds. A. Predojevic and M. W. Mitchell