Le début des années 2000 a vu la cartographie du génome humain se réaliser après 13 ans de recherche. Le défi du prochain siècle réside dans la construction du connectome humain, qui consiste à cartographier les connexions du cerveau en utilisant l’imagerie par résonance magnétique (IRM) de diffusion. Cette technique permet en effet d’étudier la matière blanche du cerveau de façon complètement non invasive. Bien que le défi soit monumental, la résolution d’une image d’IRM se situe à l’échelle macroscopique et est environ 1000 fois inférieure à la taille des axones qu’il faut cartographier. Pour aider à pallier à ce problème, ce mémoire propose une nouvelle technique de débruitage spécialement conçue pour l’imagerie de diffusion. L’algorithme Non Local Spatial and Angular Matching (NLSAM) se base sur les principes du block matching et du dictionary learning pour exploiter la redondance des données d’IRM de diffusion. Un seuillage sur les voisins angulaire est aussi réalisé à l’aide du sparse coding, où l’erreur de reconstruction en norme l2 est bornée par la variance locale du bruit. L’algorithme est aussi conçu pour gérer le biais du bruit Ricien et Chi non centré puisque les images d’IRM contiennent du bruit non Gaussien. Ceci permet ainsi d’acquérir des données d’IRM de diffusion à une plus grande résolution spatiale que présentement disponible en milieu clinique. Ce travail ouvre donc la voie à un meilleur type d’acquisition, ce qui pourrait contribuer à révéler de nouveaux détails anatomiques non discernables à la résolution spatiale présentement utilisée par la communauté d’IRM de diffusion. Ceci pourrait aussi éventuellement contribuer à identifier de nouveaux biomarqueurs permettant de comprendre les maladies dégénératives telles que la sclérose en plaques, la maladie d’Alzheimer et la maladie de Parkinson.

De nos jours, l'augmentation rapide de les vidéos crée une demande massive d'applications d'édition de vidéos. Dans cette thèse, nous résolvons plusieurs problèmes relatifs au post-traitement vidéo. Nous nous concentrons sur l'application de suppression d'objets en vidéo. Pour mener à bien cette tâche, nous l'avons divisé en deux problèmes: (1) une étape de segmentation des objets vidéo pour sélectionner les objets à supprimer et (2) une étape d'inpainting vidéo pour remplir les zones endommagées. Pour le problème de la segmentation vidéo, nous concevons un système adapté aux applications de suppression d’objets avec différentes exigences en termes de précision et d’efficacité. Notre approche repose sur la combinaison de réseaux de neurones convolutifs (CNN) pour la segmentation et de la méthode classique de suivi des masks. Nous adoptons des réseaux de segmentation d’images et les appliquons à la casse vidéo en effectuant une segmentation image par image. En exploitant à la fois les formations en ligne et hors ligne avec uniquement une annotation de première image, les réseaux sont en mesure de produire une segmentation extrêmement précise des objets vidéo. En outre, nous proposons un module de suivi de masque pour assurer la continuité temporelle et un module de liaison de masque pour assurer la cohérence de l'identité entre les trames. De plus, nous présentons un moyen simple d’apprendre la couche de dilatation dans le masque, ce qui nous aide à créer des masques appropriés pour l’application de suppression d’objets vidéo.Pour le problème d’inpainting vidéo, nous divisons notre travail en deux catégories basées sur le type de fond. En particulier, nous présentons une méthode simple de propagation de pixels guidée par le mouvement pour traiter les cas d’arrière-plan statiques. Nous montrons que le problème de la suppression d'objets avec un arrière-plan statique peut être résolu efficacement en utilisant une technique simple basée sur le mouvement. Pour traiter le fond dynamique, nous introduisons la méthode d’inpainting vidéo en optimisant une fonction d’énergie globale basée sur des patchs. Pour augmenter la vitesse de l'algorithme, nous avons proposé une extension parallèle de l'algorithme 3D PatchMatch. Pour améliorer la précision, nous intégrons systématiquement le flux optique dans le processus global. Nous nous retrouvons avec une méthode d’inpainting vidéo capable de reconstruire des objets en mouvement ainsi que de reproduire des textures dynamiques tout en fonctionnant dans des délais raisonnables.Enfin, nous combinons les méthodes de segmentation des objets vidéo et d’inpainting vidéo dans un système unifié pour supprimer les objets non souhaités dans les vidéos. A notre connaissance, il s'agit du premier système de ce type. Dans notre système, l'utilisateur n'a qu'à délimiter approximativement dans le premier cadre les objets à modifier. Ce processus d'annotation est facilité par l'aide de superpixels. Ensuite, ces annotations sont affinées et propagées dans la vidéo par la méthode de segmentation des objets vidéo. Un ou plusieurs objets peuvent ensuite être supprimés automatiquement à l’aide de nos méthodes d’inpainting vidéo. Il en résulte un outil de montage vidéo informatique flexible, avec de nombreuses applications potentielles, allant de la suppression de la foule à la correction de scènes non physiques. / Nowadays, the rapid increase of video creates a massive demand for video-based editing applications. In this dissertation, we solve several problems relating to video post-processing and focus on objects removal application in video. To complete this task, we divided it into two problems: (1) A video objects segmentation step to select which objects to remove and (2) a video inpainting step to filling the damaged regions.For the video segmentation problem, we design a system which is suitable for object removal applications with different requirements in terms of accuracy and efficiency. Our approach relies on the combination of Convolutional Neural Networks (CNNs) for segmentation and the classical mask tracking method. In particular, we adopt the segmentation networks for image case and apply them to video case by performing frame-by-frame segmentation. By exploiting both offline and online training with first frame annotation only, the networks are able to produce highly accurate video object segmentation. Besides, we propose a mask tracking module to ensure temporal continuity and a mask linking module to ensure the identity coherence across frames. Moreover, we introduce a simple way to learn the dilation layer in the mask, which helps us create suitable masks for video objects removal application.For the video inpainting problem, we divide our work into two categories base on the type of background. In particular, we present a simple motion-guided pixel propagation method to deal with static background cases. We show that the problem of objects removal with a static background can be solved efficiently using a simple motion-based technique. To deal with dynamic background, we introduce video inpainting method by optimization a global patch-based energy function. To increase the speed of the algorithm, we proposed a parallel extension of the 3D PatchMatch algorithm. To improve accuracy, we systematically incorporate the optical flow in the overall process. We end up with a video inpainting method which is able to reconstruct moving objects as well as reproduce dynamic textures while running in a reasonable time.Finally, we combine the video objects segmentation and video inpainting methods into a unified system to removes undesired objects in videos. To the best of our knowledge, this is the first system of this kind. In our system, the user only needs to approximately delimit in the first frame the objects to be edited. These annotation process is facilitated by the help of superpixels. Then, these annotations are refined and propagated through the video by the video objects segmentation method. One or several objects can then be removed automatically using our video inpainting methods. This results in a flexible computational video editing tool, with numerous potential applications, ranging from crowd suppression to unphysical scenes correction.

Dans un processus d’inférence statistique, lorsque le calcul de la fonction de vraisemblance associée aux données observées n’est pas possible, il est nécessaire de recourir à des approximations. C’est un cas que l’on rencontre très fréquemment dans certains champs d’application, notamment pour des modèles de génétique des populations. Face à cette difficulté, nous nous intéressons aux méthodes de calcul bayésien approché (ABC, Approximate Bayesian Computation) qui se basent uniquement sur la simulation de données, qui sont ensuite résumées et comparées aux données observées. Ces comparaisons nécessitent le choix judicieux d’une distance, d’un seuil de similarité et d’un ensemble de résumés statistiques pertinents et de faible dimension.Dans un contexte d’inférence de paramètres, nous proposons une approche mêlant des simulations ABC et les méthodes d’apprentissage automatique que sont les forêts aléatoires. Nous utilisons diverses stratégies pour approximer des quantités a posteriori d’intérêts sur les paramètres. Notre proposition permet d’éviter les problèmes de réglage liés à l’ABC, tout en fournissant de bons résultats ainsi que des outils d’interprétation pour les praticiens. Nous introduisons de plus des mesures d’erreurs de prédiction a posteriori (c’est-à-dire conditionnellement à la donnée observée d’intérêt) calculées grâce aux forêts. Pour des problèmes de choix de modèles, nous présentons une stratégie basée sur des groupements de modèles qui permet, en génétique des populations, de déterminer dans un scénario évolutif les évènements plus ou moins bien identifiés le constituant. Toutes ces approches sont implémentées dans la bibliothèque R abcrf. Par ailleurs, nous explorons des manières de construire des forêts aléatoires dites locales, qui prennent en compte l’observation à prédire lors de leur phase d’entraînement pour fournir une meilleure prédiction. Enfin, nous présentons deux études de cas ayant bénéficié de nos développements, portant sur la reconstruction de l’histoire évolutive de population pygmées, ainsi que de deux sous-espèces du criquet pèlerin Schistocerca gregaria. / In a statistical inferential process, when the calculation of the likelihood function is not possible, approximations need to be used. This is a fairly common case in some application fields, especially for population genetics models. Toward this issue, we are interested in approximate Bayesian computation (ABC) methods. These are solely based on simulated data, which are then summarised and compared to the observed ones. The comparisons are performed depending on a distance, a similarity threshold and a set of low dimensional summary statistics, which must be carefully chosen.In a parameter inference framework, we propose an approach combining ABC simulations and the random forest machine learning algorithm. We use different strategies depending on the parameter posterior quantity we would like to approximate. Our proposal avoids the usual ABC difficulties in terms of tuning, while providing good results and interpretation tools for practitioners. In addition, we introduce posterior measures of error (i.e., conditionally on the observed data of interest) computed by means of forests. In a model choice setting, we present a strategy based on groups of models to determine, in population genetics, which events of an evolutionary scenario are more or less well identified. All these approaches are implemented in the R package abcrf. In addition, we investigate how to build local random forests, taking into account the observation to predict during their learning phase to improve the prediction accuracy. Finally, using our previous developments, we present two case studies dealing with the reconstruction of the evolutionary history of Pygmy populations, as well as of two subspecies of the desert locust Schistocerca gregaria.

Durant ces dernières années, les technologies d’acquisition numériques n’ont cessé de se perfectionner, permettant d’obtenir des données d’une qualité toujours plus fine. Néanmoins, le signal acquis reste corrompu par des défauts qui ne peuvent être corrigés matériellement et nécessitent l’utilisation de méthodes de restauration adaptées. J'usqu’au milieu des années 2000, ces approches s’appuyaient uniquement sur un traitement local du signal détérioré. Avec l’amélioration des performances de calcul, le support du filtre a pu être étendu à l’ensemble des données acquises en exploitant leur caractère autosimilaire. Ces approches non locales ont principalement été utilisées pour restaurer des données régulières et structurées telles que des images. Mais dans le cas extrême de données irrégulières et non structurées comme les nuages de points 3D, leur adaptation est peu étudiée à l’heure actuelle. Avec l’augmentation de la quantité de données échangées sur les réseaux de communication, de nouvelles méthodes non locales ont récemment été proposées. Elles utilisent un modèle a priori extrait à partir de grands ensembles d’échantillons pour améliorer la qualité de la restauration. Néanmoins, ce type de méthode reste actuellement trop coûteux en temps et en mémoire. Dans cette thèse, nous proposons, tout d’abord, d’étendre les méthodes non locales aux nuages de points 3D, en définissant une surface de points capable d’exploiter leur caractère autosimilaire. Nous introduisons ensuite une nouvelle structure de données, le CovTree, flexible et générique, capable d’apprendre les distributions d’un grand ensemble d’échantillons avec une capacité de mémoire limitée. Finalement, nous généralisons les méthodes de restauration collaboratives appliquées aux données 2D et 3D, en utilisant notre CovTree pour apprendre un modèle statistique a priori à partir d’un grand ensemble de données. / In recent years, digital technologies allowing to acquire real world objects or scenes have been significantly improved in order to obtain high quality datasets. However, the acquired signal is corrupted by defects which can not be rectified materially and require the use of adapted restoration methods. Until the middle 2000s, these approaches were only based on a local process applyed on the damaged signal. With the improvement of computing performance, the neighborhood used by the filter has been extended to the entire acquired dataset by exploiting their self-similar nature. These non-local approaches have mainly been used to restore regular and structured data such as images. But in the extreme case of irregular and unstructured data as 3D point sets, their adaptation is few investigated at this time. With the increase amount of exchanged data over the communication networks, new non-local methods have recently been proposed. These can improve the quality of the restoration by using an a priori model extracted from large data sets. However, this kind of method is time and memory consuming. In this thesis, we first propose to extend the non-local methods for 3D point sets by defining a surface of points which exploits their self-similar of the point cloud. We then introduce a new flexible and generic data structure, called the CovTree, allowing to learn the distribution of a large set of samples with a limited memory capacity. Finally, we generalize collaborative restoration methods applied to 2D and 3D data by using our CovTree to learn a statistical a priori model from a large dataset.

Stereo vision is a very useful, yet challenging technology for a wide variety of applications. One of the greatest challenges is meeting the computational demands of stereo vision applications that require real-time performance. The FPGA (Field Programmable Gate Array) is a readily-available technology that allows many stereo vision methods to be implemented while meeting the strict real-time performance requirements of some applications. Some of the best results have been obtained using non-parametric stereo correlation methods, such as the rank and census transform. Yet relatively little work has been done to study these methods or to propose new algorithms based on the same principles for improved stereo correlation accuracy or reduced resource requirements. This dissertation describes the sparse census and sparse rank transforms, which significantly reduce the cost of implementation while maintaining and in some case improving correlation accuracy. This dissertation also proposes the generalized census and generalized rank transforms, which opens up a new class of stereo vision transforms and allows the stereo system to be even more optimized, often reducing the hardware resource requirements. The proposed stereo methods are analyzed, providing both quantitative and qualitative results for comparison to existing algorithms. These results show that the computational complexity of local stereo methods can be significantly reduced while maintaining very good correlation accuracy. A hardware architecture for the implementation of the proposed algorithms is also described and the actual resource requirements for the algorithms are presented. These results confirm that dramatic reductions in hardware resource requirements can be achieved while maintaining high stereo correlation accuracy. This work proposes the multi-bit census, which provides improved pixel discrimination as compared to the census, and leads to improved correlation accuracy with some stereo configurations. A rotation-invariant census transform is also proposed and can be used in applications where image rotation is possible.

This thesis examines the concept of image self-similarity and provides solutions to various associated inverse problems such as resolution enhancement and missing fractal codes. In general, many real-world inverse problems are ill-posed, mainly because of the lack of existence of a unique solution. The procedure of providing acceptable unique solutions to such problems is known as regularization. The concept of image prior, which has been of crucial importance in image modelling and processing, has also been important in solving inverse problems since it algebraically translates to the regularization procedure. Indeed, much recent progress in imaging has been due to advances in the formulation and practice of regularization. This, coupled with progress in optimization and numerical analysis, has yielded much improvement in computational methods of solving inverse imaging problems. Historically, the idea of self-similarity was important in the development of fractal image coding. Here we show that the self-similarity properties of natural images may be used to construct image priors for the purpose of addressing certain inverse problems. Indeed, new trends in the area of non-local image processing have provided a rejuvenated appreciation of image self-similarity and opportunities to explore novel self-similarity-based priors. We first revisit the concept of fractal-based methods and address some open theoretical problems in the area. This includes formulating a necessary and sufficient condition for the contractivity of the block fractal transform operator. We shall also provide some more generalized formulations of fractal-based self-similarity constraints of an image. These formulations can be developed algebraically and also in terms of the set-based method of Projection Onto Convex Sets (POCS). We then revisit the traditional inverse problems of single frame image zooming and multi-frame resolution enhancement, also known as super-resolution. Some ideas will be borrowed from newly developed non-local denoising algorithms in order to formulate self-similarity priors. Understanding the role of scale and choice of examples/samples is also important in these proposed models. For this purpose, we perform an extensive series of numerical experiments and analyze the results. These ideas naturally lead to the method of self-examples, which relies on the regularity properties of natural images at different scales, as a means of solving the single-frame image zooming problem. Furthermore, we propose and investigate a multi-frame super-resolution counterpart which does not require explicit motion estimation among video sequences.

This thesis examines the concept of image self-similarity and provides solutions to various associated inverse problems such as resolution enhancement and missing fractal codes. In general, many real-world inverse problems are ill-posed, mainly because of the lack of existence of a unique solution. The procedure of providing acceptable unique solutions to such problems is known as regularization. The concept of image prior, which has been of crucial importance in image modelling and processing, has also been important in solving inverse problems since it algebraically translates to the regularization procedure. Indeed, much recent progress in imaging has been due to advances in the formulation and practice of regularization. This, coupled with progress in optimization and numerical analysis, has yielded much improvement in computational methods of solving inverse imaging problems. Historically, the idea of self-similarity was important in the development of fractal image coding. Here we show that the self-similarity properties of natural images may be used to construct image priors for the purpose of addressing certain inverse problems. Indeed, new trends in the area of non-local image processing have provided a rejuvenated appreciation of image self-similarity and opportunities to explore novel self-similarity-based priors. We first revisit the concept of fractal-based methods and address some open theoretical problems in the area. This includes formulating a necessary and sufficient condition for the contractivity of the block fractal transform operator. We shall also provide some more generalized formulations of fractal-based self-similarity constraints of an image. These formulations can be developed algebraically and also in terms of the set-based method of Projection Onto Convex Sets (POCS). We then revisit the traditional inverse problems of single frame image zooming and multi-frame resolution enhancement, also known as super-resolution. Some ideas will be borrowed from newly developed non-local denoising algorithms in order to formulate self-similarity priors. Understanding the role of scale and choice of examples/samples is also important in these proposed models. For this purpose, we perform an extensive series of numerical experiments and analyze the results. These ideas naturally lead to the method of self-examples, which relies on the regularity properties of natural images at different scales, as a means of solving the single-frame image zooming problem. Furthermore, we propose and investigate a multi-frame super-resolution counterpart which does not require explicit motion estimation among video sequences.

Cette thèse s’intéresse à diverses composantes du traitement et de l’analyse d’images par méthodes non locales. Ces méthodes sont basées sur la redondance d’information présente dans d’autres images, et utilisent des algorithmes de recherche de correspondance, généralement basés sur l’utilisation patchs, pour extraire et transférer de l’information depuis ces images d’exemples. Ces approches, largement utilisées par la communauté de vision par ordinateur, sont souvent limitées par le temps de calcul de l’algorithme de recherche, appliqué à chaque pixel, et par la nécessité d’effectuer un prétraitement ou un apprentissage pour utiliser de grandes bases de données.Pour pallier ces limites, nous proposons plusieurs méthodes générales, sans apprentissage,rapides, et qui peuvent être facilement adaptées à diverses applications de traitement et d’analyse d’images naturelles ou médicales. Nous introduisons un algorithme de recherche de correspondances permettant d’extraire rapidement des patchs d’une grande bibliothèque d’images 3D, que nous appliquons à la segmentation d’images médicales. Pour utiliser de façon similaire aux patchs,des présegmentations en superpixels réduisant le nombre d’éléments de l’image,nous présentons une nouvelle structure de voisinage de superpixels. Ce nouveau descripteur permet d’utiliser efficacement les superpixels dans des approches non locales. Nous proposons également une méthode de décomposition régulière et précise en superpixels. Nous montrons comment évaluer cette régularité de façon robuste, et que celle-ci est nécessaire pour obtenir de bonnes performances de recherche de correspondances basées sur les superpixels. / This thesis focuses on several aspects of image analysis and processing with non local methods. These methods are based on the redundancy of information that occurs in other images, and use matching algorithms, that are usually patch-based, to extract and transfer information from the example data. These approaches are widely used by the computer vision community, and are generally limited by the computational time of the matching algorithm, applied at the pixel scale, and by the necessity to perform preprocessing or learning steps to use large databases. To address these issues, we propose several general methods, without learning, fast, and that can be easily applied to different image analysis and processing applications on natural and medical images. We introduce a matching algorithm that enables to quickly extract patches from a large library of 3D images, that we apply to medical image segmentation. To use a presegmentation into superpixels that reduces the number of image elements, in a way that is similar to patches, we present a new superpixel neighborhood structure. This novel descriptor enables to efficiently use superpixels in non local approaches. We also introduce an accurate and regular superpixel decomposition method. We show how to evaluate this regularity in a robust manner, and that this property is necessary to obtain good superpixel-based matching performances.

Modificações na fisiologia do organismo da mulher ocorrem durante a gravidez em conseqüência às alterações hormonais, anatômicas e metabólicas. No sistema circulatório a modificação mais significativa é o aumento do débito cardíaco a partir do primeiro trimestre da gestação. Mulheres portadoras de cardiopatias podem apresentar graves complicações durante o período gestacional devido à inapropriada adaptação à sobrecarga hemodinâmica, mesmo em pacientes consideradas em capacidade funcional favorável, no início da gestação. A literatura carece de estudos dos efeitos dos anestésicos locais com ou sem vasoconstritor utilizados nos procedimentos odontológicos, sobre os parâmetros cardiovasculares de mulheres gestantes portadoras de valvopatias e seus conceptos. A escassez científica fez deste tema nosso objetivo de estudo: avaliar e analisar parâmetros da cardiotocografia, como freqüência cardíaca, motilidade fetal e contrações uterinas e de pressão arterial e eletrocardiográficos da gestante portadora de doença valvar reumática quando submetida à anestesia local com lidocaína, com e sem vasoconstritor, durante procedimento odontológico restaurador. Para tanto, a monitorização ambulatorial da pressão arterial (MAPA) e a eletrocardiografia ambulatorial (Holter) materna, ambas obtidas durante 24 horas e a cardiotografia (CTG) de 31 portadoras de cardiopatia reumática entre a 28ª e 37ª semana de gestação, nos períodos: (1) basal - 60 minutos antes do procedimento para MAPA e Holter e 20 minutos para CTG; (2) procedimento - 56+15,5minutos (média+desvio padrão); (3) pós-procedimento - 20 minutos; e (4) média das 24 horas para freqüência cardíaca e extra-sístoles e média da vigília e do sono para pressão arterial, permitiu a análise da variação desses parâmetros, utilizando-se lidocaína 2% sem vasoconstritor e lidocaína 2% com epinefrina 1:100.000, compondo-as em dois grupos. Demonstrou-se redução significativa nos valores de freqüência cardíaca materna durante o procedimento somente quando comparado aos demais períodos (p<0,001). Quando comparados os dois grupos, não houve diferença (p>0,05). Houve ocorrência de arritmia cardíaca em 9 (29,0%) pacientes, sendo 7 (41,8%) do grupo que recebeu anestesia com adrenalina. A pressão arterial materna não apresentou diferença quando comparamos os períodos ou os grupos (p>0,05). O mesmo ocorreu (p>0,05) nas análises comparativas dos parâmetros fetais obtidos por meio da CTG - número de contrações, nível e variabilidade da linha de base, número de acelerações da freqüência cardíaca fetal e padrão de reatividade fetal. Concluiu-se que o uso da lidocaína 2% associado à adrenalina mostrou-se seguro em procedimento odontológico durante a gestação de mulheres com cardiopatia valvar reumática. / During pregnancy, the organic systems of a woman are subjected to physiological modifications consequential to hormonal, anatomic and metabolical alterations. The most significant modification in the circulatory system is an increased cardiac output from the first three months of gestation. Women with heart disease may present with severe complications during the gestational period, because of inappropriate adaptation of her body to this hemodynamic overload, even those patients who are thought to have an appropriate functional capacity during early pregnancy. There are scant studies in the literature on the effects of local anesthetics, with and without vasoconstrictor, used in dental procedures on the cardiovascular variables of pregnant women with valvar disease, as well as on their concepti. Driven by this shortage, we decided to have this subject studied, by assessing and analyzing cardiotachographic parameters, such as heart rate, fetal motility and uterine contractions, in addition to blood pressure and electrocardiographic variables, in pregnant women with rheumatic valvar disease who undergo local anesthesia with lidocaine, with and without vasoconstrictor, during restorative dental procedure. For this, 31 rheumatic heart disease patients who were in their 28th to 37th week of gestation, had 24-hour ambulatory monitoring of their blood pressure (BP) and Holter electrocardiography (Holter-ECG), and cardiotocography (CTG), performed during: (1) baseline - 60 minutes before the procedure for BP and Holter- ECG monitoring, and 20 minutes before the procedure for CTG; (2) procedure - 56±15.5 minutes (mean±SD); (3) post-procedure - 20 minutes; and (4) mean 24-hour heart rate and extrasystoles measurement, and mean wake and sleeping periods BP monitoring. Variation of the above variables was analyzed in two groups, one with infusion of a 2% solution of lidocaine with vasoconstrictor, and the other with infusion of a 2% solution of lidocaine with epinephrine 1:100.000. The maternal heart rate values obtained during the procedure showed a significant reduction only in comparison with the other time periods (P<0.001). The comparison of the two groups did not reveal any significant difference (P>0.05). Cardiac arrhythmia was detected in 9 (29.0%) patients, 7 of them (41.8%) from the group who received anesthetics with epinephrine. Maternal blood pressure did not show any significant difference neither between time periods, nor between groups (P>0.05). The same occurred in the comparative analysis of the fetal parameters obtained during CTG -number of contractions, level and variability from baseline, number of fetal heart rate accelerations and fetal reactivity pattern. Our conclusion was that the use of 2% solution of lidocaine in association with epinephrine proved safe during dental procedure in pregnant women with rheumatic valvar cardiopathy.

Modificações na fisiologia do organismo da mulher ocorrem durante a gravidez em conseqüência às alterações hormonais, anatômicas e metabólicas. No sistema circulatório a modificação mais significativa é o aumento do débito cardíaco a partir do primeiro trimestre da gestação. Mulheres portadoras de cardiopatias podem apresentar graves complicações durante o período gestacional devido à inapropriada adaptação à sobrecarga hemodinâmica, mesmo em pacientes consideradas em capacidade funcional favorável, no início da gestação. A literatura carece de estudos dos efeitos dos anestésicos locais com ou sem vasoconstritor utilizados nos procedimentos odontológicos, sobre os parâmetros cardiovasculares de mulheres gestantes portadoras de valvopatias e seus conceptos. A escassez científica fez deste tema nosso objetivo de estudo: avaliar e analisar parâmetros da cardiotocografia, como freqüência cardíaca, motilidade fetal e contrações uterinas e de pressão arterial e eletrocardiográficos da gestante portadora de doença valvar reumática quando submetida à anestesia local com lidocaína, com e sem vasoconstritor, durante procedimento odontológico restaurador. Para tanto, a monitorização ambulatorial da pressão arterial (MAPA) e a eletrocardiografia ambulatorial (Holter) materna, ambas obtidas durante 24 horas e a cardiotografia (CTG) de 31 portadoras de cardiopatia reumática entre a 28ª e 37ª semana de gestação, nos períodos: (1) basal - 60 minutos antes do procedimento para MAPA e Holter e 20 minutos para CTG; (2) procedimento - 56+15,5minutos (média+desvio padrão); (3) pós-procedimento - 20 minutos; e (4) média das 24 horas para freqüência cardíaca e extra-sístoles e média da vigília e do sono para pressão arterial, permitiu a análise da variação desses parâmetros, utilizando-se lidocaína 2% sem vasoconstritor e lidocaína 2% com epinefrina 1:100.000, compondo-as em dois grupos. Demonstrou-se redução significativa nos valores de freqüência cardíaca materna durante o procedimento somente quando comparado aos demais períodos (p<0,001). Quando comparados os dois grupos, não houve diferença (p>0,05). Houve ocorrência de arritmia cardíaca em 9 (29,0%) pacientes, sendo 7 (41,8%) do grupo que recebeu anestesia com adrenalina. A pressão arterial materna não apresentou diferença quando comparamos os períodos ou os grupos (p>0,05). O mesmo ocorreu (p>0,05) nas análises comparativas dos parâmetros fetais obtidos por meio da CTG - número de contrações, nível e variabilidade da linha de base, número de acelerações da freqüência cardíaca fetal e padrão de reatividade fetal. Concluiu-se que o uso da lidocaína 2% associado à adrenalina mostrou-se seguro em procedimento odontológico durante a gestação de mulheres com cardiopatia valvar reumática. / During pregnancy, the organic systems of a woman are subjected to physiological modifications consequential to hormonal, anatomic and metabolical alterations. The most significant modification in the circulatory system is an increased cardiac output from the first three months of gestation. Women with heart disease may present with severe complications during the gestational period, because of inappropriate adaptation of her body to this hemodynamic overload, even those patients who are thought to have an appropriate functional capacity during early pregnancy. There are scant studies in the literature on the effects of local anesthetics, with and without vasoconstrictor, used in dental procedures on the cardiovascular variables of pregnant women with valvar disease, as well as on their concepti. Driven by this shortage, we decided to have this subject studied, by assessing and analyzing cardiotachographic parameters, such as heart rate, fetal motility and uterine contractions, in addition to blood pressure and electrocardiographic variables, in pregnant women with rheumatic valvar disease who undergo local anesthesia with lidocaine, with and without vasoconstrictor, during restorative dental procedure. For this, 31 rheumatic heart disease patients who were in their 28th to 37th week of gestation, had 24-hour ambulatory monitoring of their blood pressure (BP) and Holter electrocardiography (Holter-ECG), and cardiotocography (CTG), performed during: (1) baseline - 60 minutes before the procedure for BP and Holter- ECG monitoring, and 20 minutes before the procedure for CTG; (2) procedure - 56±15.5 minutes (mean±SD); (3) post-procedure - 20 minutes; and (4) mean 24-hour heart rate and extrasystoles measurement, and mean wake and sleeping periods BP monitoring. Variation of the above variables was analyzed in two groups, one with infusion of a 2% solution of lidocaine with vasoconstrictor, and the other with infusion of a 2% solution of lidocaine with epinephrine 1:100.000. The maternal heart rate values obtained during the procedure showed a significant reduction only in comparison with the other time periods (P<0.001). The comparison of the two groups did not reveal any significant difference (P>0.05). Cardiac arrhythmia was detected in 9 (29.0%) patients, 7 of them (41.8%) from the group who received anesthetics with epinephrine. Maternal blood pressure did not show any significant difference neither between time periods, nor between groups (P>0.05). The same occurred in the comparative analysis of the fetal parameters obtained during CTG -number of contractions, level and variability from baseline, number of fetal heart rate accelerations and fetal reactivity pattern. Our conclusion was that the use of 2% solution of lidocaine in association with epinephrine proved safe during dental procedure in pregnant women with rheumatic valvar cardiopathy.