Classification of hepatic metastasis in enhanced CT images by dipolar decision tree

- Cette étude a pour but de réaliser une classification des métastases hépatiques, en imagerie scanner. Les régions d'intérêt analysées représentent du tissu sain, et quatre types de métastases. Pour chaque patient, trois acquisitions sont réalisées (sans injection de produit de contraste, aux phases artérielle et portale après injection). La méthode comporte une première étape de caractérisation par analyse de texture, suivie d'une classification des régions. La méthode de classification utilisée est basée sur les arbres de décision dipolaires. Dans cette méthode, chaque noeud de l'arbre correspond à un test multivariable (hyperplan). La recherche de l'hyperplan optimal est basée sur la séparation des dipôles (paire de vecteurs de paramètres de l'ensemble d'apprentissage). Les résultats préliminaires montrent que la qualité de classification augmente quand le temps d'acquisition des images est pris en compte, et qu'elle est supérieure à celle obtenue par d'autres méthodes de classification

Couplage d'un modèle vasculaire bi-niveau et d'un modèle d'acquisition d'images : application à la simulation d'IRM dynamique du Carcinome Hépatocellulaire

La modélisation physiologique permet de mieux comprendre les images médicales et de mettre en évidence, dans l'image, des marqueurs de la pathologie. Dans cet article, nous proposons de coupler un modèle de la vascularisation hépatique à un modèle d'acquisition d'Images de - Résonance Magnétique (IRM), et d'appliquer ces modèles à la simulation d'IRM dynamique du Carcinome Hépatocellulaire (CHC). Le modèle vasculaire intègre les propriétés anatomiques et fonctionnelles clos vaisseaux, modifiées au cours du développement tumoral (densité vasculaire, débits, perméabilité, etc). Il permet de simuler la propagation de différents produits de contraste, ou tenant compte de leurs principales propriétés physiques et magnétiques, aux niveaux macro- et micro-vasculaire. Les images simulées à clos temps d'acquisition différents (phase artérielle, phase portale) présentent clos contrastes proches de ceux observés sur clos images réelles

Vascular system modeling in parallel environment - distributed and shared memory approaches.

International audienceThis paper presents two approaches in parallel modeling of vascular system development in internal organs. In the first approach, new parts of tissue are distributed among processors and each processor is responsible for perfusing its assigned parts of tissue to all vascular trees. Communication between processors is accomplished by passing messages, and therefore, this algorithm is perfectly suited for distributed memory architectures. The second approach is designed for shared memory machines. It parallelizes the perfusion process during which individual processing units perform calculations concerning different vascular trees. The experimental results, performed on a computing cluster and multicore machines, show that both algorithms provide a significant speedup

Parallel computing in modeling of magnetic resonance imaging

L'objet de cette thèse est la modélisation computationnelle de l'Imagerie par Résonance Magnétique (IRM), appliquée à l'imagerie des réseaux vasculaires. Les images sont influencées par la géométrie des vaisseaux mais aussi par le flux sanguin. Par ailleurs, outre la qualité des modèles développés, il est important que les calculs soient performants. C'est pourquoi, le calcul parallèle est utilisé pour gérer ce type de problèmes complexes. Dans cette thèse, trois solutions sont proposées. La première concerne les algorithmes parallèles pour la modélisation des réseaux vasculaires. Des algorithmes dédiés à différentes architectures sont proposés. Le premier est basé sur le modèle de passage de messages pour les machines à mémoires distribuées. La parallélisation concerne l'irrigation de nouvelles zones de tissu par les vaisseaux existants. Le deuxième algorithme est dédié aux machines à mémoire partagée. Il parallélise également le processus de perfusion mais des processeurs différents se chargent de gérer les différents arbres vasculaires. Le troisième algorithme est une combinaison des approches précédentes offrant une solution pour les architectures parallèles hybrides. Les algorithmes proposés permettent d'accélérer considérablement la croissance des réseaux vasculaires complexes, ce qui rend possible la simulation de structures vasculaires plus précises, en un temps raisonnable et aide à améliorer le modèle vasculaire et à tester plus facilement différents jeux de paramètres. Une nouvelle approche de modélisation computationnelle des flux en IRM est également proposée. Elle combine le calcul de flux par la méthode de Lattice Boltzmann, la simulation IRM par le suivi temporel de magnétisations locales, ainsi qu'un nouvel algorithme de transport des magnétisations. Les résultats montrent qu'une telle approche intègre naturellement l'influence du flux dans la modélisation IRM. Contrairement aux travaux de la littérature, aucun mécanisme additionnel n'est nécessaire pour considérer les artéfacts de flux, ce qui offre une grande facilité d'extension du modèle. Les principaux avantages de cette méthode est sa faible complexité computationnelle, son implémentation efficace, qui facilitent le lancement des simulations en utilisant différents paramètres physiologiques ou paramètres d'acquisition des images. La troisième partie du travail de thèse a consisté à appliquer le modèle d'imagerie de flux à des réseaux vasculaires complexes en combinant les modèles de vaisseaux, de flux et d'acquisition IRM. Les algorithmes sont optimisés à tous les niveaux afin d'être performants sur des architectures parallèles. Les possibilités du modèle sont illustrées sur différents cas. Cette démarche de modélisation peut aider à mieux interpréter les images IRM grâce à l'intégration, dans les modèles, de connaissances variées allant de la vascularisation des organes jusqu'à la formation de l'image en passant par les propriétés des flux sanguins.This PhD thesis concerns computer modeling of magnetic resonance imaging (MRI). The main attention is centered on imaging of vascular structures. Such imaging is influenced not only by vascular geometries but also by blood flow which has to been taken into account in modeling. Next to the question about the quality of developed models, the challenge lies also in the demand for high performance computing. Thus, in order to manage computationally complex problems, parallel computing is in use. In the thesis three solutions are proposed. The first one concerns parallel algorithms of vascular network modeling. Algorithms for different architectures are proposed. The first algorithm is based on the message passing model and thus, it is suited for distributed memory architectures. It parallelizes the process of connecting new parts of tissue to existing vascular structures. The second algorithm is designed for shared memory machines. It also parallelizes the perfusion process, but individual processors perform calculations concerning different vascular trees. The third algorithm combines message passing and shared memory approaches providing solutions for hybrid parallel architectures. Developed algorithms are able to substantially speed up the time-demanded simulations of growth of complex vascular networks. As a result, more elaborate and precise vascular structures can be simulated in a reasonable period of time. It can also help to extend the vascular model and to test multiple sets of parameters. Secondly, a new approach in computational modeling of magnetic resonance (MR) flow imaging is proposed. The approach combines the flow computation by lattice Boltzmann method, MRI simulation by following discrete local magnetizations in time and a new magnetization transport algorithm together. Results demonstrate that such an approach is able to naturally incorporate the flow influence in MRI modeling. As a result, in the proposed model, no additional mechanism (unlike in prior works) is needed to consider flow artifacts, what implies its easy extensibility. In combination with its low computational complexity and efficient implementation, the solution is a user-friendly and manageable at different levels tool which facilitates running series of simulations with different physiological and imaging parameters. The goal of the third solution is to apply the proposed MR flow imaging model on complex vascular networks. To this aim, models of vascular networks, flow behavior and MRI are combined together. In all the model components, computations are adapted to be performed at various parallel architectures. The model potential and possibilities of simulations of flow and MRI in complex vascular structures are shown. The model aims at explaining and exploring MR image formation and appearance by the combined knowledge from many processes and systems, starting from vascular geometry, through flow patterns and ending on imaging technology.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Simulation of biphasic CT findings in hepatic cellular carcinoma by a two-level physiological model.

International audienceIn this paper, we present a two-level physiological model that is able to reflect morphology and function of vascular networks, in clinical images. Our approach results from the combination of a macroscopic model, providing simulation of the growth and pathological modifications of vascular network, and a microvascular model, based on compartmental approach, which simulates blood and contrast medium transfer through capillary walls. The two-level model is applied to generate biphasic computed tomography of hepatocellular carcinoma. A contrast-enhanced sequence of simulated images is acquired, and enhancement curves extracted from normal and tumoral regions are compared to curves obtained from in vivo images. The model offers the potential of finding early indicators of disease in clinical vascular images

Modèle de transport de molécules dans le foie (application à l'IRM dynamique)

L'analyse d'images est une méthode non invasive utilisée dans la définition du diagnostic des lésions du foie. En complément de l analyse visuelle effectuée par les radiologues, certaines méthodes quantitatives, telles que l'analyse de texture peuvent donner des résultats encourageants dans la caractérisation des tumeurs. Nous proposons de coupler un modèle bi-niveau du foie, prenant en compte des paramètres physiologiques et pathologiques, avec un modèle d'acquisition d'IRM, dans le but de comprendre certaines relations entre les caractéristiques de l'image et le développement tumoral. Un modèle pharmacocinétique basé physiologie (PBPK) distribué axialement et adapté au foie, permet de simuler la distribution de molécules d agents de contraste spécifiques au foie. Il est couplé à un modèle macroscopique de l organe et de sa vascularisation. Ce modèle multi-échelle permet la simulation de modifications d'ordre pathologique liées au développement du carcinome hépatocellulaire, et la simulation des images IRM correspondantes.Image analysis is a noninvasive technique used to define the diagnosis of liver lesions. In addition to the visual inspection brought by radiologists, some quantitative methods such as texture analysis can also give encouraging results regarding tumor characterization. We propose to couple a bi-level model of the liver, which takes into account some physiological and pathological parameters, with the simulation of dynamic MRI acquisition, in order to understand some relations between image characteristics and the tumor development. A new axially-distributed Physiologically-Based PharmacoKinetic (PBPK) model, adapted to the liver, enables the simulation of the distribution of liver-specific contrast agents. This model is coupled with a macroscopic model of the liver and of its vascularisation. The multiscale model allows for i) simulations of pathological modifications related to the development of the hepatocellular carcinoma, and ii) simulations of corresponding MR images.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Hierarchical Parallel Approach in Vascular Network Modeling - Hybrid MPI+OpenMP Implementation.

This paper presents a two-level parallel algorithm of vascular network development. At the outer level, tasks (newly appeared parts of tissue) are spread over processing nodes. Each node attempts to connect/disconnect its assigned parts of tissue in all vascular trees. Communication between nodes is accomplished by a message passing paradigm. At the inner level, subtasks concerning different vascular trees (e.g. arterial and venous) within each task are parallelized by a shared address space paradigm. The solution was implemented on a computing cluster of multi-core nodes with mixed MPI+OpenMP support. The experimental results show that the algorithm provides a significant improvement in computational performance compared with a pure MPI implementation

Vascular Network Modeling - Improved Parallel Implementation on Computing Cluster.

International audienceIn this paper, an improved parallel algorithm of vascular network modeling is presented. The new solution is based on a more decentralized approach. Moreover, in order to accelerate the simulation of vascular growth process both the dynamic load balancing and periodic rebuildings of vascular trees were introduced. The presented method was implemented on a computing cluster with the use of the MPI standard. The experimental results show that the improved algorithm results in better speedup thus making it possible to introduce more physiological details and also perform simulations with a greater number of vessels and cells. Furthermore, the presented approach can bring the model closer to reality where the analogous vascular processes can be also parallel

Texture-based characterization of arterialization in simulated MRI of hypervascularized liver tumors.

International audienceThe use of quantitative imaging for the characterization of hepatic tumors in MRI can improve the diagnosis and therefore the treatment. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. In particular, the lesion arterialization is prominent in the resulting contrast between normal and tumoral tissues in contrast-enhanced images. In order to identify this influence, we propose a multiscale model of liver dynamic contrast-enhanced MRI, consisting of a model of the organ coupled with a model of the image acquisition. A sensitivity analysis of the model to the arterial flow has enabled us to emphasize the existence of relationships between texture parameters in simulated arterial-phase MR images, and the arterialization phenomena involved in carcinogenesis

GPU-based computational modeling of magnetic resonance imaging of vascular structures

International audienceMagnetic resonance imaging (MRI) is one of the most important diagnostic tools in modern medicine. Since it is a high-cost and highly-complex imaging modality, computational models are frequently built to enhance its understanding as well as to support further development. However, such models often have to be simplified to complete simulations in a reasonable time. Thus, the simulations with high spatial/temporal resolutions, with any motion consideration (like blood flow) and/or with 3D objects usually call for using parallel computing environments. In this paper, we propose to use graphics processing units (GPUs) for fast simulations of MRI of vascular structures. We apply a CUDA environment which supports general purpose computation on GPU (GPGPU). The data decomposition strategy is applied and thus the parts of each virtual object are spread over the GPU cores. The GPU cores are responsible for calculating the influence of blood flow behavior and MRI events after successive time steps. In the proposed approach, different data layouts, memory access patterns, and other memory improvements are applied to efficiently exploit GPU resources. Computational performance is thoroughly validated for various vascular structures and different NVIDIA GPUs. Results show that MRI simulations can be accelerated significantly thanks to GPGPU. The proposed GPU-based approach may be easily adopted in the modeling of other flow related phenomena like perfusion, diffusion or transport of contrast agents