OntoVIP: An ontology for the annotation of object models used for medical image simulation.

International audienceThis paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository

A virtual imaging platform for multi-modality medical image simulation.

International audienceThis paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011

Novel geometry for X-Ray diffraction mammary imaging: experimental validation on a breast phantom

International audienceMammography is the first tool in breast cancer diagnosis. Its contrast relies on the difference of X-Ray attenuation in healthy and diseased tissues, which is quite limited. This leads to frequent false-positive or inconclusive results and requires further testing. X-Ray Diffraction provides information about molecular structure and can differentiate between healthy and cancerous breast tissues. It can thus be used in synergy with existing imaging methods to provide complementary diagnosis-relevant insight.We present a novel geometry of such an imaging system and its validation on a breast phantom composed ofolive oil and beef muscle, imitating respectively the molecular structure of healthy and cancerous breast tissue.Our system combines energy-dispersive and angle-dispersive X-Ray diffraction by means of an energy-resolvedCdZnTe detector and a multi-slit collimation in order to achieve depth-resolved imaging.The position of the tube with beef muscle inside the oil was varied in this experiment. The obtained results aresatisfactory regarding the estimated position of the tube which is very promising for future ex-vivo experiments on human breast tissue samples. Further investigations are carried out on dose reduction and reliable classification algorithms in order to prepare this method for clinical applications

Charge Sharing on Monolithic CdZnTe Gamma-Ray Detectors: A Simulation Study

International audienceMonolithic CdZnTe gamma-ray detectors are used to build gamma cameras for nuclear medicine imaging but their energy resolution is currently limited by charge sharing between adjacent anodes. For this work, charge sharing is simulated using Ulysse, a numerical model that takes into account the physical processes of charge creation by ionizing radiation and charge transport within the semiconductor detector. The charge carrier cloud size, following the gamma-ray photon interaction, is computed by the Monte Carlo method. Electron cloud diffusion and charge induction on the electrodes are computed by the finite element method. This study shows that the electron diffusion strongly influences the final electron cloud diameter

Reconstruction itérative en tomographie à rayons X pour une géométrie inverse avec sources distribuées

International audienceA conventional Cone Beam Computed Tomography (CBCT) architecture is composed of a single source and a large detector to aquire a full sinogram of the object. In opposite a Multi-Source Inverse Geometry Computed Tomography (MS-IGCT) architecture propose to use several sources and a small detector to acquire several truncated sinograms of the object. Using few sources while keeping a small detector size is a key issue for technological, financial and in some cases, dose reasons. However in this configuration the object reconstruction induces to solve an ill-posed and ill-conditionned problem. We propose a regularized iterative algorithm which is able to reconstruct the object volume from sinograms acquired with an optimized MS-IGCT : we will demonstrate the performance of the proposed algorithm when we reduce the size of the detector and the number of sources. In a second step we show that taking the inverse of the noise covariance matrix into account reduces dramatically metals artifacts due to the high density of the reconstructed object. Realistically simulated CT data is reconstructed with the proposed algorithm and the results are compared to those obtained by filtered backprojection (FBP).Une architecture «Cone Beam Computed Tomography» (CBCT) conventionnelle est constituée d'une seule source et d'un grand détecteur afin d'acquérir un sinogramme complet de l'objet. Par opposition, une géométrie inverse (en tomographie) utilise plusieurs sources distribuées permettant d'acquérir avec un petit détecteur plusieurs sinogrammes tronqués de l'objet. Ce type d'architecture est communément appelée «Multi Source Inverse Geometry Computed Tomography » (MS-IGCT). Pour des raisons technologiques, financières et dans certains cas, de dose, utiliser peu de sources tout en gardant une taille du détecteur réduite est un enjeu essentiel. Cependant dans ce type de configuration la reconstruction de l'objet demande de résoudre un problème qui est mal-posé et mal conditionné. Nous proposons donc un algorithme itératif régularisé permettant de reconstruire un volume objet à partir des sinogrammes acquis par une architecture MS-IGCT optimisée : nous démontrons les performances de l'algorithme proposé lorsque nous réduisons la taille du détecteur ainsi que le nombre de sources. Dans un second temps nous montrons comment la prise en compte, dans la formulation du problème inverse, de la matrice inverse de la covariance du bruit permet de réduire drastiquement les artefacts métalliques dûs aux parties très denses de l'objet reconstruit. À partir de données simulées de façon réaliste, nous comparons les reconstructions obtenues avec l'algorithme proposé avec une méthode de rétroprojection filtrée classique