Medical ultrasound image reconstruction using distributed compressive sampling

International audienceThis paper investigates ultrasound (US) radiofrequency (RF) signal recovery using the distributed compressed sampling framework. The “correlation” between the RF signals forming a RF image is exploited by assuming that they have the same sparse support in the 1D Fourier transform, with different coefficient values. The method is evaluated using an experimental US image. The results obtained are shown to improve a previously proposed recovery method, where the correlation between RF signals was taken into account by assuming the 2D Fourier transform of the RF image sparse

Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this paper, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e. high quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on non-simulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography

A sparse reconstruction framework for Fourier-based plane wave imaging

International audienceUltrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct high-quality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality

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

Segmentation in echocardiographic imaging using parametric level set model driving by the statistics of the radiofrequency signal

L objectif de cette thèse est de développer et de valider une méthode de traitement d image permettant d'obtenir la segmentation et le suivi de structures cardiaques tel que le myocarde en échographie ultrasonore de radiofréquence. Dans un premier temps, nous proposons une étude statistique du signal radiofréquence. Cette étude nous a permis de définir et de valider une distribution statistique permettant de modéliser la statistique du signal ultrasonore pour des régions tissulaires et sanguines. Dans un deuxième temps, nous exploitons le modèle statistique proposé dans un formalisme de contour actif variationnel afin d effectuer la segmentation d images échographiques. Le modèle ainsi utilisé est basé sur la minimisation d une fonctionnelle d énergie dérivée d un critère de maximum de vraisemblance. Le modèle de contour actif a été implémenté en utilisant la méthode des ensembles de niveaux. L'évaluation menée sur des simulations numériques et des données échocardiographiques a montré la supériorité de ce modèle sur un modèle basé sur une statistique Gaussienne, en particulier pour la détection du myocarde. Finalement, afin de segmenter l ensemble d une séquence, nous proposons d insérer des informations a priori spatio-temporelles dans le formalisme d ensemble de niveaux. Pour ce faire, nous avons développé un modèle d ensemble de niveaux paramétrique basé sur une méthode de collocation exploitant des fonctions de base radiale. Cette approche permet ainsi de contraindre l évolution du contour actif implicite par l exploitation d un filtre de Kalman. L'intérêt de cette démarche a été illustré sur des simulations numériques et des données échocardiographiques.The objective of this work is to design and validate methods dedicated to the segmentation and tracking of cardiac structures such as myocardial regions from echocardiographc radiofrequency (RF) data. Firstly, we performed a statistical study of the RF signal. From this study, we defined and validated a statistical distribution optimized for the modelization of the statistics of the RF signal for both blood and tissue regions. We then exploited this statistical model in a variational active contour framework to perform the segmentation of echocardiographic images. The model we used is based on the minimization of an energy functional derived from a maximum likelihood criterion. The corresponding active contour has been implemented using level set model. Results obtained from both simulation and in vivo data show the ability of our model to detect myocardial regions. Finally, in order to perform the segmentation of echocardiographic image sequences, we introduce spatio-temporal constraints into the level set framework. We thus proposed a new parametric level set model based on a collocation method using radial basis functions. This approach allows to constrain the level set evolution using Kalman filtering. The interest of such method has been illustrated from both simulations and in vivo data.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Traitement et analyse d'images cardiaques: Traité IC2 Série Information et Science du vivant

International audienc

Multi-modality Cardiac Imaging: Processing and Analysis

International audienceThe imaging of moving organs such as the heart, in particular, is a real challenge because of its movement. This book presents current and emerging methods developed for the acquisition of images of moving organs in the five main medical imaging modalities: conventional X-rays, computed tomography (CT), magnetic resonance imaging (MRI), nuclear imaging and ultrasound. The availability of dynamic image sequences allows for the qualitative and quantitative assessment of an organ’s dynamics, which is often linked to pathologies

Caractérisation fonctionnelle d'un heptapeptide cyclique inhibiteur d'activités b-lactamases et de protéines liant la pénicilline

A partir d'une enzyme, la b-Iactamase, nous avons généré un anticorps antiidiotypique à activité b-Iactamase que nous avons nommé 9G4H9. En comparant les résidus essentiels à l'activité catalytique de la b-Iactamase, nous avons proposé un modèle de site actif pour l'anticorps 9G4H9 dans lequel nous avons identifié les résidus arginine 24, sérine 26, lysine 27, sérine 28 et acide glutamique en position 98 susceptible d'être impliqués dans l'activité catalytique. Nous avons montré l'implication de tous les résidus identifiés dans l'activité catalytique hormis le résidu lysine en position 27. L'anticorps 9G4H9 a servi de cible pour la sélection d'un peptide inhibiteur par phage-display, nommé Pep90, d'activités b-Iactamases et d'activités parentes. Au cours de cette étude, nous avons caractérisé les modalités d'interaction de Pep90 respectivement avec l'anticorps catalytique 9G4H9, le scFv 9G4H9, différentes classes de b-Iactamases et DD-peptidases.9G4H9, a catalytic antibody displaying b-lactamase-Iike activity, has been elicited by the anti-idiotypic approach using b-Iactamase as first antigen. We proposed an active site model for antibody 9G4H9 in which we find residues arginine 24, serine 26, lysine 27, serine 28 and glutamic acid 98 that could be involved in b-Iactamase activity. We showed that ail the residues are involved in catalysis except residue lysine 27. ln the second part of the work, antibody 9G4H9 was used as target to screen b-Iactamase activity inhibitor among cyclic heptapeptide bank displayed on bacteriophage M13. One of the phage-displayed peptide (pep90) issued from the selection procedures was shown to be a competitive inhibitor of the b-Iactamase activity of the anti-idiotypic antibody, with a Ki = 38uM. We showed that Pep90 interact with several class of penicillin-binding protein, thus opening routes to the design of antibiotic-like molecules.COMPIEGNE-BU (601592101) / SudocSudocFranceF

Dynamic segmentation in ultrasound radiofrequency echocardiography

L'objectif de cette thèse est le développement de techniques de segmentation d'images ultrasonores de radiofréquence (RF) en échographie cardiaque. Le premier aspect de ce travail concerne la détection du produit de contraste ultrasonore. Une méthode paramétrique basée sur l'analyse spectrale autorégressive (AR) a été proposée. Il a été montré sur simulations et images in vitro que l'approche proposée est peu sensible aux variation de concentration et du MI instrumental. Le deuxième aspect de ce travail concerne la segmentation de séquences d'images cardiaques RF. En se basant sur l'analyse AR, il est montré que le contenu spectral du signal RF apporte une information supplémentaire relativement à l'enveloppe seule. Une méthode de segmentation est ensuite proposée, basée sur les ensembles de niveaux couplés avec un recalage affine. La méthode a été validée sur simulations et sur de séquences in vitro, montrant son intérêt pour la segmentation et le suivi du muscle cardiaque.The goal of this Ph. D. thesis is the development of techniques of radiofrequency (RF) image segmentation in cardiac echography. The first part of this work is dedicated to the detection of ultraound contrast agent. A parametric method based on local spectral autoregressive (AR) analysis of the RF signal is proposed. It is shown on simulations and in vitro images thats the proposed approach is stable with respect to concentration of the agent and the instrumental MI. The second part of the work concerns segmentation of cardiac RF sequences. Based on AR spectral analysis, it is shown that the spectral contents of the RF signal brings complementary information, as compared to the envelope image alone. A segmentation method is subsequently introduced, based on the level set framework coupled with affine registration. The method is validated on numerical simulations as well as on ultrasound in vivo sequences, showing its interest for segmentation and tracking of the cardiac muscle.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF