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Current methods for estimation of proton density fat fraction (PDFF) of the liver using magnitude magnetic resonance (MR) imaging face the challenge of correctly estimating it when fat is the dominant molecule, i.e. PDFF is more than 50%. Therefore, the accuracy of the methods is limited to half-range operation. We introduce a method based on neural networks for regression capable of estimating over the full range of fat fractions. We built a neural network based on the angles and distances between the data in the discrete MR signal (ADALIFE), using these as features associated to different PDFFs and as input for the network. Tests were performed assessing ADALIFE against dual echo, triple echo, and especially Multi-interference, a state-of-the-art method to estimate PDFFs, with simulated signals at various signal-to-noise (SNR) values. Results were compared in order to verify repeatability and agreement using regression analysis, Bland-Altman and REC curves. Results for Multi-interference were similar to its in-vivo literature, showing the relevance of a simulation. ADALIFE was able to correctly estimate fat fractions up to 100%, breaking the current paradigm for full-range estimation using only off-line post processing. Within half-range, our method outperformed Multi-interference in repeatability and agreement, with narrower limits of agreement and lower expected error at any SNR. / Os métodos atuais para estimação de gordura hepática por densidade de prótons (PDFF) utilizando imagem de magnitude de ressonância magnética (RM) enfrentam o desafio de estimar corretamente quando a gordura é a molécula dominante, ou seja, PDFF é maior que 50%. Assim, a acurácia desses métodos é limitada a meio intervalo de operação. Apresentamos aqui um método baseado em redes neurais para regressão capaz de estimar pelo intervalo completo de frações de gordura. Construímos uma rede neural baseada nos ângulos e distâncias entre os dados do sinal discreto da imagem de RM (ADALIFE), usando esses atributos associados a diferentes valores de PDFF, com sinais simulados considerando diferentes relações sinal-ruído (SNR). Resultados foram comparados para verificar a repetibilidade e concordância através de análise de regressão, Bland- Altman e curvas de característica de erro de regressão (REC). Resultados para o método Multi-interferência (estado-da-arte) foram similares aos relatados in vivo pela literatura, ressaltando a relevância das simulações. ADALIFE foi capaz de estimar corretamente frações de gordura até 100%, quebrando o paradigma para intervalo completo de operação utilizando apenas processamento posterior à aquisição de imagens ou sinais. Considerando meio intervalo, nosso método superou o estado-da-arte em termos de repetibilidade e concordância, com limites mais estreitos e menor erro esperado em qualquer SNR.

Current methods for estimation of proton density fat fraction (PDFF) of the liver using magnitude magnetic resonance (MR) imaging face the challenge of correctly estimating it when fat is the dominant molecule, i.e. PDFF is more than 50%. Therefore, the accuracy of the methods is limited to half-range operation. We introduce a method based on neural networks for regression capable of estimating over the full range of fat fractions. We built a neural network based on the angles and distances between the data in the discrete MR signal (ADALIFE), using these as features associated to different PDFFs and as input for the network. Tests were performed assessing ADALIFE against dual echo, triple echo, and especially Multi-interference, a state-of-the-art method to estimate PDFFs, with simulated signals at various signal-to-noise (SNR) values. Results were compared in order to verify repeatability and agreement using regression analysis, Bland-Altman and REC curves. Results for Multi-interference were similar to its in-vivo literature, showing the relevance of a simulation. ADALIFE was able to correctly estimate fat fractions up to 100%, breaking the current paradigm for full-range estimation using only off-line post processing. Within half-range, our method outperformed Multi-interference in repeatability and agreement, with narrower limits of agreement and lower expected error at any SNR. / Os métodos atuais para estimação de gordura hepática por densidade de prótons (PDFF) utilizando imagem de magnitude de ressonância magnética (RM) enfrentam o desafio de estimar corretamente quando a gordura é a molécula dominante, ou seja, PDFF é maior que 50%. Assim, a acurácia desses métodos é limitada a meio intervalo de operação. Apresentamos aqui um método baseado em redes neurais para regressão capaz de estimar pelo intervalo completo de frações de gordura. Construímos uma rede neural baseada nos ângulos e distâncias entre os dados do sinal discreto da imagem de RM (ADALIFE), usando esses atributos associados a diferentes valores de PDFF, com sinais simulados considerando diferentes relações sinal-ruído (SNR). Resultados foram comparados para verificar a repetibilidade e concordância através de análise de regressão, Bland- Altman e curvas de característica de erro de regressão (REC). Resultados para o método Multi-interferência (estado-da-arte) foram similares aos relatados in vivo pela literatura, ressaltando a relevância das simulações. ADALIFE foi capaz de estimar corretamente frações de gordura até 100%, quebrando o paradigma para intervalo completo de operação utilizando apenas processamento posterior à aquisição de imagens ou sinais. Considerando meio intervalo, nosso método superou o estado-da-arte em termos de repetibilidade e concordância, com limites mais estreitos e menor erro esperado em qualquer SNR.

L’IRM quantitative recouvre l’ensemble des méthodes permettant de mesurer des paramètres physiques accessibles en Résonance Magnétique Nucléaire. Elle offre un bénéfice par rapport à l’imagerie en pondération classiquement utilisée, notamment pour la détection, la caractérisation physiopathologique mais aussi pour le suivi thérapeutique des pathologies. Malgré ce potentiel avéré connu de longue date, ces méthodes restent peu utilisées dans la routine clinique. La raison principale est la longueur des acquisitions par rapport à l’approche classique. Les paramètres physiques que nous souhaitons étudier plus particulièrement sont le temps de relaxation longitudinal (T₁), transversal (T₂), le coefficient de diffusion apparent (ADC), et la densité de protons (DP). Malgré la possibilité d’atteindre une meilleure qualité d’images, ces cartographies in vivo sont quasiment inexistantes dans la littérature au-delà de 3T car leur implémentation nécessite de surmonter un certain nombre de limites spécifiques aux IRM ultra-haut champs (UHF). Au travers de ce projet de thèse, une méthode d’imagerie quantitative basée sur les états de configurations (QuICS) a été implémentée, pour déterminer ces paramètres quantitatifs de façon simultanée sous fortes contraintes propres aux UHF. L’approche a été optimisée dans le but d’obtenir des cartographies fiables et rapides. Le potentiel de la méthode a été démontré dans un premier temps in vitro sur un noyau tel que le sodium démontrant des propriétés complexes à cartographier. Puis dans un second temps, des acquisitions ont été réalisées sur proton, in vivo, en un temps d’acquisition compatible avec une utilisation en routine clinique à 7T. L’application d’une telle méthode d’IRM quantitative à UHF sur des populations permettra d’ouvrir de nouvelles voies d’études pour le futur. / Quantitative MRI refers to methods able to measure different physical parameters accessible in Nuclear Magnetic Resonance. It offers benefits compared to weighting imaging commonly used, for the detection, the pathophysiological characterization but also for the therapeutic follow-up of pathologies for example. Despite this long-established potential, these methods remain little used in clinical routine. The main reason is the long acquisition time compared to the classical approach. The physical parameters that we will study more particularly are the longitudinal (T₁), transverse (T₂) relaxation time, the apparent diffusion coefficient (ADC), and the proton density (DP). Despite the possibility to achieve a better image quality, these in vivo mappings are virtually non-existent in the literature beyond 3T because their implementation requires overcom-ing a number of specific ultra-high-field (UHF) MRI limits. Through this thesis project, a Quantitative Imaging method using Configuration States (QuICS) was implemented under strong UHF constraints, to determine these parameters simultaneously. The technique has been optimized to obtain fast and reliable maps. The potential of the method was first demon-strated in vitro on a nucleus such as sodium, exhibiting complex properties. As a second step, acquisitions were performed in proton, in vivo, in an clinically-relevant acquisition time, compatible with a routine use at 7T for population imaging. The application of such a method of quantitative MRI to UHF will open new research possibilities for the future.

No description available.

Pour le diagnostic et la stratification de la fibrose hépatique, la rigidité du foie est un biomarqueur quantitatif estimé par des méthodes d'élastographie. L'élastographie par ondes de cisaillement (« shear wave », SW) utilise des ultrasons médicaux non invasifs pour évaluer les propriétés mécaniques du foie sur la base des propriétés de propagation des ondes de cisaillement. La vitesse des ondes de cisaillement (« shear wave speed », SWS) et l'atténuation des ondes de cisaillement (« shear wave attenuation », SWA) peuvent fournir une estimation de la viscoélasticité des tissus. Les tissus biologiques sont intrinsèquement viscoélastiques et un modèle mathématique complexe est généralement nécessaire pour calculer la viscoélasticité en imagerie SW. Le calcul précis de l'atténuation est essentiel, en particulier pour une estimation précise du module de perte et de la viscosité. Des études récentes ont tenté d'augmenter la précision de l'estimation du SWA, mais elles présentent encore certaines limites. Comme premier objectif de cette thèse, une méthode de décalage de fréquence revisitée a été développée pour améliorer les estimations fournies par la méthode originale de décalage en fréquence [Bernard et al 2017]. Dans la nouvelle méthode, l'hypothèse d'un paramètre de forme décrivant les caractéristiques spectrales des ondes de cisaillement, et assumé initialement constant pour tous les emplacements latéraux, a été abandonnée permettant un meilleur ajustement de la fonction gamma du spectre d'amplitude. En second lieu, un algorithme de consensus d'échantillons aléatoires adaptatifs (« adaptive random sample consensus », A-RANSAC) a été mis en œuvre pour estimer la pente du paramètre de taux variable de la distribution gamma afin d’améliorer la précision de la méthode. Pour valider ces changements algorithmiques, la méthode proposée a été comparée à trois méthodes récentes permettant d’estimer également l’atténuation des ondes de cisaillements (méthodes de décalage en fréquence, de décalage en fréquence en deux points et une méthode ayant comme acronyme anglophone AMUSE) à l'aide de données de simulations ou fantômes numériques. Également, des fantômes de gels homogènes in vitro et des données in vivo acquises sur le foie de canards ont été traités. Comme deuxième objectif, cette thèse porte également sur le diagnostic précoce de la stéatose hépatique non alcoolique (NAFLD) qui est nécessaire pour prévenir sa progression et réduire la mortalité globale. À cet effet, la méthode de décalage en fréquence revisitée a été testée sur des foies humains in vivo. La performance diagnostique de la nouvelle méthode a été étudiée sur des foies humains sains et atteints de la maladie du foie gras non alcoolique. Pour minimiser les sources de variabilité, une méthode d'analyse automatisée faisant la moyenne des mesures prises sous plusieurs angles a été mise au point. Les résultats de cette méthode ont été comparés à la fraction de graisse à densité de protons obtenue de l'imagerie par résonance magnétique (« magnetic resonance imaging proton density fat fraction », MRI-PDFF) et à la biopsie du foie. En outre, l’imagerie SWA a été utilisée pour classer la stéatose et des seuils de décision ont été établis pour la dichotomisation des différents grades de stéatose. Finalement, le dernier objectif de la thèse consiste en une étude de reproductibilité de six paramètres basés sur la technologie SW (vitesse, atténuation, dispersion, module de Young, viscosité et module de cisaillement). Cette étude a été réalisée chez des volontaires sains et des patients atteints de NAFLD à partir de données acquises lors de deux visites distinctes. En conclusion, une méthode robuste de calcul du SWA du foie a été développée et validée pour fournir une méthode de diagnostic de la NAFLD. / For diagnosis and staging of liver fibrosis, liver stiffness is a quantitative biomarker estimated by elastography methods. Ultrasound shear wave (SW) elastography utilizes noninvasive medical ultrasound to assess the mechanical properties of the liver based on the monitoring of the SW propagation. SW speed (SWS) and SW attenuation (SWA) can provide an estimation of tissue viscoelasticity. Biological tissues are inherently viscoelastic in nature and a complex mathematical model is usually required to compute viscoelasticity in SW imaging. Accurate computation of attenuation is critical, especially for accurate loss modulus and viscosity estimation. Recent studies have made attempts to increase the precision of SWA estimation, but they still face some limitations. As a first objective of this thesis, a revisited frequency-shift method was developed to improve the estimates provided by the original implementation of the frequency-shift method [Bernard et al 2017]. In the new method, the assumption of a constant shape parameter of the gamma function describing the SW magnitude spectrum has been dropped for all lateral locations, allowing a better gamma fitting. Secondly, an adaptive random sample consensus algorithm (A-RANSAC) was implemented to estimate the slope of the varying rate parameter of the gamma distribution to improve the accuracy of the method. For the validation of these algorithmic changes, the proposed method was compared with three recent methods proposed to estimate SWA (frequency-shift, two-point frequency-shift and AMUSE methods) using simulation data or numerical phantoms. In addition, in vitro homogenous gel phantoms and in vivo animal (duck) liver data were processed. As a second objective, this thesis also aimed at improving the early diagnosis of nonalcoholic fatty liver disease (NAFLD), which is necessary to prevent its progression and decrease the overall mortality. For this purpose, the revisited frequency-shift method was tested on in vivo human livers. The new method's diagnosis performance was investigated with healthy and NAFLD human livers. To minimize sources of variability, an automated analysis method averaging measurements from several angles has been developed. The results of this method were compared to the magnetic resonance imaging proton density fat fraction (MRI-PDFF) and to liver biopsy. SWA imaging was used for grading steatosis and cut-off decision thresholds were established for dichotomization of different steatosis grades. As a third objective, this thesis is proposing a reproducibility study of six SW-based parameters (speed, attenuation, dispersion, Young’s modulus, viscosity and shear modulus). The assessment was performed in healthy volunteers and NAFLD patients using data acquired at two separate visits. In conclusion, a robust method for computing the liver’s SWA was developed and validated to provide a diagnostic method for NAFLD.