710 research outputs found

    Extraction de la courbe d'entrée à partir des images TEP du coeur chez le petit animal pour la modélisation pharmacocinétique

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    Dans cette thèse, nous présentons l'ensemble de nos contributions relatives à la mise en oeuvre et à la validation de techniques d'extraction d'une courbe de l'activité d'un traceur radioactif, dite courbe d'entrée (CE), à partir des images enregistrées par tomographie d'émission par positrons (TEP). Cette courbe est primordiale pour la quantification de paramètres physiologiques et métaboliques comme le métabolisme du glucose au niveau du myocarde chez le petit animal. La modalité d'imagerie TEP sert à déceler, à des phases souvent précoces, le dysfonctionnement d'un organe par un examen médical. L'examen consiste en une injection d'un élément radioactif, émetteur de positrons attachés à une molécule caractérisée par les mêmes propriétés chimiques et biologiques qu'une molécule naturelle, et de suivre son activité temporelle. La quantité du traceur mesurée dans le plasma sanguin en fonction du temps constitue la CE, tandis que la radioactivité mesurée dans les tissus par la TEP constitue la réponse des tissus. La CE et la réponse des tissus sont les fonctions fondamentales d'un modèle mathématique appelé "le modèle pharmacocinétique" qui estime les paramètres physiologiques et métaboliques. Habituellement la CE est obtenue d'une manière invasive par un prélèvement sanguin qui se fait parallèlement à l'acquisition des données. En plus, elle nécessite une chaîne de préparation pour enregistrer la concentration du traceur radioactif dans le plasma et une fréquence d'échantillonnage corrélée avec le découpage de la séquence d'images. Dans le cadre de nos recherches, nous avons développé des techniques d'extraction de la CE directement à partir d'une séquence d'images TEP. Cette approche présente l'avantage d'être non-invasive et permet un contrôle sur la fréquence d'échantillonnage temporel. Néanmoins, la résolution spatiale, les limites physiques, les limites physiologiques et les limites méthodologiques reliées à la reconstruction d'images sont des facteurs qui détériorent la qualité de la courbe. Dans un premier temps, nous avons appliqué un concept probabiliste à l'intérieur de deux régions d'intérêts (Ris) tracées sur la séquence d'images délimitant le ventricule gauche et le myocarde. La méthode estime la fraction du sang dans les deux régions pour déterminer une CE non dégradée par les effets mentionnés précédemment. Cette approche a permis de corriger la courbe en tenant compte des effets causés par la contamination spatiale. Dans un deuxième temps, nous avons travaillé sur la réduction de l'effet du mouvement du coeur et des poumons sur la qualité de la CE. Pour cela, nous avons utilisé une acquisition de données synchronisée par rapport à l'électrocardiogramme (ECG). Cette acquisition nécessite un suivi automatique des RIs sur les différents cadres synchronisés. Pour remédier aux effets de la faible résolution spatiale des images, nous avons développé un modèle particulier d'un contour déformable qui répond aux faiblesses des images TEP. Notre modèle est capable de délimiter le ventricule gauche et le myocarde sur les images d'une façon quasi-automatique. Finalement, nous avons généralisé l'idée de l'extraction de la CE pour différents traceurs tels que le glucose marqué au fluor ([indice supérieur 18]F-FDG), l'ammoniaque marqué à l'azote ([indice supérieur 13]N-ammoniaque), le [indice supérieur 82] rubidium ([indice supérieur 82]Rb) et l'acétate marqué au carbone ([indice supérieur 11]C-acétate). Le modèle que nous avons développé est basé sur l'estimation de la CE par l'analyse en composante indépendante (ACI) et la distribution gaussienne généralisée (DGG). Tous nos résultats pour le traceur [indice supérieur 18]F-FDG sont comparés à la méthode de référence classique, à savoir le prélèvement sanguin. Les résultats de l'extraction de la CE par l'ACI ont été comparés à ceux extraits par la méthode de référence et par la moyenne de l'activité d'une RI segmentée manuellement sur les images. Les résultats montrent l'apport de la méthode sur l'amélioration de la courbe lorsque celle-ci est dégradée par la contamination croisée. Le travail accompli dans cette thèse montre la possibilité de contourner les limites de l'imagerie TEP par l'utilisation d'approches statistiques dans le but d'extraire une CE fiable. Les méthodes développées représentent une alternative à la méthode invasive d'échantillonnage sanguin

    Quantification of 18F-FDG PET kinetic parameters using an image-derived input function and multimodal integration with resting-state fMRI metrics

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    Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints.Metabolic demand associated with resting-state brain activity is one of the main focus of neuroscience research. Task-free brain activation has been found to exhibit coherent spatial patterns, and the associated glucose consumption is predominant if compared to task activation. However, a complete characterization of the link between energy and function in the brain is still missing. The aim of this thesis project was to explore novel strategies for the integration between metabolic measures coming from Positron Emission Tomography based on fluorodeoxyglucose ([18F]FDG PET) and functional information extracted from resting-state Functional Magnetic Resonance Imaging (rsfMRI) measures. This was done adopting two different perspectives. On one hand, it was verified how metabolic and functional networks, inferred from time-series correlation across brain regions, relate to each other. On the other hand, across-subject similarity between sets of metabolic parameters and functional features was assessed. The analysis was performed on a dataset provided by Washington University in St.Louis, consisting of non-simultaneous PET and MR acquisitions on a large cohort of subjects. A first part of the work focused on [18F]FDG data. An Image-derived input function (IDIF) was extracted from the internal carotid arteries. This was later used for microparameter estimation with Variational Bayesian approach. Across-subjects correlation matrices were obtained for subjects series of K1 and k3 values. Moreover, average metabolic connectivity matrix was extracted from [18F]FDG parcel-level TACs. Similarly, from fMRI data, average functional connectivity matrix was extracted. Regional Homogeneity (ReHo) and Global Functional Connectivity (GFC) were estimated and across-subjects connectivity matrices were obtained for both parameters. Time-series connectivity matrices coming from both PET and fMRI images were used to assess similarity between metabolic and functional networks, whereas across-subject connectivity matrices were used to compare metabolic and functional parameters. To agevolate comparison, embedding was used on both timeseries and across-subjects connectivity: this was based on application of a gaussian kernel, followed by calculation of the Laplacian Eigenmaps, a nonlinear dimensionality reduction techinque. Resulting manifolds are called gradients in neuroscience, and are commonly used to study functional architecture in the brain. From a network perspective, metabolic and functional gradients exhibited significant correlation, and the regions in which they overlapped the most belong to visual and sensorimotor networks. Similar results were found between all combinations of [18F]FDG microparameters and fMRI features gradients, implying that both local and global functional relationship in the brain may be associated with specific metabolic fingerprints

    Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

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    Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease

    Non-Negative Blind Source Separation Algorithm Based on Minimum Aperture Simplicial Cone

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    International audienceWe address the problem of Blind Source Separation (BSS) when the hidden sources are Nonnegative (N-BSS). In this case, the scatter plot of the mixed data is contained within the simplicial cone generated by the columns of the mixing matrix. The proposed method, termed SCSA-UNS for Simplicial Cone Shrinking Algorithm for Unmixing Non-negative Sources, aims at estimating the mixing matrix and the sources by fitting a Minimum Aperture Simplicial Cone (MASC) to the cloud of mixed data points. SCSA-UNS is evaluated on both independent and correlated synthetic data and compared to other N-BSS methods. Simulations are also performed on real Liquid Chromatography-Mass Spectrum (LC-MS) data for the metabolomic analysis of a chemical sample, and on real dynamic Positron Emission Tomography (PET) images, in order to study the pharmacokinetics of the [18F]-FDG (FluoroDeoxyGlucose) tracer in the brain

    Motion Correction and Pharmacokinetic Analysis in Dynamic Positron Emission Tomography

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    This thesis will focus on two important aspects of dynamic Positron Emission Tomography (PET): (i) Motion-compensation , and (ii) Pharmacokinetic analysis (also called parametric imaging) of dynamic PET images. Both are required to enable fully quantitative PET imaging which is increasingly finding applications in the clinic. Motion-compensation in Dynamic Brain PET Imaging: Dynamic PET images are degraded by inter-frame and intra-frame motion artifacts that can a ffect the quantitative and qualitative analysis of acquired PET data. We propose a Generalized Inter-frame and Intra-frame Motion Correction (GIIMC) algorithm that uni fies in one framework the inter-frame motion correction capability of Multiple Acquisition Frames and the intra-frame motion correction feature of (MLEM)-type deconvolution methods. GIIMC employs a fairly simple but new approach of using time-weighted average of attenuation sinograms to reconstruct dynamic frames. Extensive validation studies show that GIIMC algorithm outperforms conventional techniques producing images with superior quality and quantitative accuracy. Parametric Myocardial Perfusion PET Imaging: We propose a novel framework of robust kinetic parameter estimation applied to absolute flow quantification in dynamic PET imaging. Kinetic parameter estimation is formulated as nonlinear least squares with spatial constraints problem where the spatial constraints are computed from a physiologically driven clustering of dynamic images, and used to reduce noise contamination. The proposed framework is shown to improve the quantitative accuracy of Myocardial Perfusion (MP) PET imaging, and in turn, has the long-term potential to enhance capabilities of MP PET in the detection, staging and management of coronary artery disease

    Neuroinformatics in Functional Neuroimaging

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    This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology

    Metabolic and Blood Flow Properties of Functional Brain Networks Using Human Multimodal Neuroimaging

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    The brain has a high energetic cost to support neuronal activity, requiring both oxygen and glucose supply from the cerebral vascular system. Additionally, the brain functions through complex patterns of interconnectivity between neuronal assemblies giving rise to functional network architectures that can be investigated across multiple spatial scales. Different brain regions have different roles and importance within these network architectures, with some regions exhibiting more global importance by being involved in cross-network communication while other being predominantly involved in local connections. There are indications that regions exhibiting a more global role in inter networks connectivity are characterized by a higher and more efficient metabolic profile, leading to differences in metabolic properties when compared to more locally connected regions. Understanding the link between oxygen/glucose metabolism and functional features of brain network architectures, across different spatial scales, is of primary importance. This thesis consists of three original studies combining human brain resting-state multimodal neuroimaging and transcriptional data to investigate the glucose/oxygen metabolic costs of brain functional connectivity. We quantified glucose metabolism from positron emission tomography, and oxygen metabolism and functional connectivity from magnetic resonance imaging. In the first study, we highlight how the oxygen/glucose metabolism of brain regions can non-linearly relate to their functional hubness, within the resting-state networks of the brain across a nested hierarchy. We found that an increase in oxygen/glucose metabolism is associated with a non-linear increase in functional hubness where increase rates are both network- and scale-dependent. In the second study, we show specific transcriptional signatures that characterize the oxygen/glucose metabolic costs of regions involved in network global versus local centrality. This study highlights the different metabolic profiles of local and global regions, with gene expression related to oxidative metabolism and synaptic pathways being enriched in association with spatial patterns in common with resting blood flow and metabolism (oxygen and glucose) and globally-connected regions. In the third study, we demonstrate that there are oxygen/glucose metabolic costs to the functional integration and segregation of resting-state networks. We highlight that the metabolic costs of functional integration could reflect the hierarchical organization of the brain from unimodal to transmodal regions
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