Optimisation robuste et application à la reconstruction du réseau artériel humain

Cardiovascular diseases are currently the leading cause of mortality in developed countries, due to the constant increase in risk factors in the population. Several prospective and retrospective studies have shown that arterial stiffness is an important predictor factor of these diseases. Unfortunately, these parameters are difficult to measure experimentally. We propose a numerical approach to determine the arterial stiffness of an arterial network using a patient specificone-dimensional model of the temporal variation of the section and blood flow of the arteries. The proposed approach estimates the optimal parameters of the reduced model, including the arterial stiffness, using non-invasive measurements such MRI, echotracking and tonometry aplanation. Different optimization results applied on experimental cases will be presented. In order to determine the robustness of the model towards its parameters, an uncertainty analysis hasbeen also carried out to measure the contribution of the model input parameters, alone or by interaction with other inputs, to the variation of model output, here the arterial pulse pressure. This study has shown that the numerical pulse pressure is a reliable indicator that can help to diagnose arterial hypertension.We can then provide the practitioner a robust patient-specific tool allowing an early and reliable diagnosis of cardiovascular diseases based on a non-invasive examLes maladies cardiovasculaires représentent actuellement une des premières causes de mortalité dans les pays développés liées à l’augmentation constante des facteurs de risques dans les populations. Différentes études cliniques ont montré que la rigidité artérielle était un facteur prédictif important pour ces maladies.Malheureusement, il s’avère difficile d’accéder expérimentalement à la valeur de ce paramètre. On propose une approche qui permet de déterminer numériquement la rigidité artérielle d’un réseau d’artères à partir d’un modèle monodimensionnel personnalisé de la variation temporelle de la section et du débit sanguin des artères. L’approche proposée résout le problème inverse associé au modèle réduit pour déterminer la rigidité de chaque artère, à l’aide de mesures non invasives de type IRM, echotracking ettonométrie d’aplanation.Pour déterminer la robustesse du modèle construit vis à vis de ses paramètres, une quantification d’incertitude a été effectuée pour mesurer la contribution de ceux-ci, soit seuls soit par interaction, à la variation de la sortie du modèle, ici la pression pulsée. Cette étude a montré que la pression pulsée numérique est un indicateur numérique robuste pouvant aider au diagnostic de l’hypertension artérielle.Nous pouvons ainsi offrir au praticien un outil numérique robuste et peu coûteux permettant un diagnostic précoce et fiable des risques cardiovasculaires pour tout patient simplement à partir d’un examen non invasi

Robust optimization and application to the reconstruction of the human arterial system

Les maladies cardiovasculaires représentent actuellement une des premières causes de mortalité dans les pays développés liées à l’augmentation constante des facteurs de risques dans les populations. Différentes études cliniques ont montré que la rigidité artérielle était un facteur prédictif important pour ces maladies.Malheureusement, il s’avère difficile d’accéder expérimentalement à la valeur de ce paramètre. On propose une approche qui permet de déterminer numériquement la rigidité artérielle d’un réseau d’artères à partir d’un modèle monodimensionnel personnalisé de la variation temporelle de la section et du débit sanguin des artères. L’approche proposée résout le problème inverse associé au modèle réduit pour déterminer la rigidité de chaque artère, à l’aide de mesures non invasives de type IRM, echotracking ettonométrie d’aplanation.Pour déterminer la robustesse du modèle construit vis à vis de ses paramètres, une quantification d’incertitude a été effectuée pour mesurer la contribution de ceux-ci, soit seuls soit par interaction, à la variation de la sortie du modèle, ici la pression pulsée. Cette étude a montré que la pression pulsée numérique est un indicateur numérique robuste pouvant aider au diagnostic de l’hypertension artérielle.Nous pouvons ainsi offrir au praticien un outil numérique robuste et peu coûteux permettant un diagnostic précoce et fiable des risques cardiovasculaires pour tout patient simplement à partir d’un examen non invasifCardiovascular diseases are currently the leading cause of mortality in developed countries, due to the constant increase in risk factors in the population. Several prospective and retrospective studies have shown that arterial stiffness is an important predictor factor of these diseases. Unfortunately, these parameters are difficult to measure experimentally. We propose a numerical approach to determine the arterial stiffness of an arterial network using a patient specificone-dimensional model of the temporal variation of the section and blood flow of the arteries. The proposed approach estimates the optimal parameters of the reduced model, including the arterial stiffness, using non-invasive measurements such MRI, echotracking and tonometry aplanation. Different optimization results applied on experimental cases will be presented. In order to determine the robustness of the model towards its parameters, an uncertainty analysis hasbeen also carried out to measure the contribution of the model input parameters, alone or by interaction with other inputs, to the variation of model output, here the arterial pulse pressure. This study has shown that the numerical pulse pressure is a reliable indicator that can help to diagnose arterial hypertension.We can then provide the practitioner a robust patient-specific tool allowing an early and reliable diagnosis of cardiovascular diseases based on a non-invasive exa

A Robust and Subject-Specific Hemodynamic Model of the Lower Limb Based on Noninvasive Arterial Measurements

Cardiovascular diseases are currently the leading cause of mortality in the population of developed countries, due to the constant increase in cardiovascular risk factors, such as high blood pressure, cholesterol, overweight, tobacco use, lack of physical activity, etc. Numerous prospective and retrospective studies have shown that arterial stiffening is a relevant predictor of these diseases. Unfortunately, the arterial stiffness distribution across the human body is difficult to measure experimentally. We propose a numerical approach to determine the arterial stiffness distribution of an arterial network using a subject-specific one-dimensional model. The proposed approach calibrates the optimal parameters of the reduced-order model, including the arterial stiffness, by solving an inverse problem associated with the noninvasive in vivo measurements. An uncertainty quantification analysis has also been carried out to measure the contribution of the model input parameters variability, alone or by interaction with other inputs, to the variation of clinically relevant hemodynamic indices, here the arterial pulse pressure. The results obtained for a lower limb model, demonstrate that the numerical approach presented here can provide a robust and subject-specific tool to the practitioner, allowing an early and reliable diagnosis of cardiovascular diseases based on a noninvasive clinical examination

A Robust and Subject-Specific Hemodynamic Model of the Lower Limb Based on Noninvasive Arterial Measurements

Cardiovascular diseases are currently the leading cause of mortality in the population of developed countries, due to the constant increase in cardiovascular risk factors, such as high blood pressure, cholesterol, overweight, tobacco use, lack of physical activity, etc. Numerous prospective and retrospective studies have shown that arterial stiffening is a relevant predictor of these diseases. Unfortunately, the arterial stiffness distribution across the human body is difficult to measure experimentally. We propose a numerical approach to determine the arterial stiffness distribution of an arterial network using a subject-specific one-dimensional model. The proposed approach calibrates the optimal parameters of the reduced-order model, including the arterial stiffness, by solving an inverse problem associated with the noninvasive in vivo measurements. An uncertainty quantification analysis has also been carried out to measure the contribution of the model input parameters variability, alone or by interaction with other inputs, to the variation of clinically relevant hemodynamic indices, here the arterial pulse pressure. The results obtained for a lower limb model, demonstrate that the numerical approach presented here can provide a robust and subject-specific tool to the practitioner, allowing an early and reliable diagnosis of cardiovascular diseases based on a noninvasive clinical examination

Detection of an Image in a Video Sequence*, **

The purpose of this work is to discuss possible methods to retrieve a frame in a source video sequence starting from a given image query (for example, a screen shot of a movie scene). In order to do that, we develop and test different approaches that rely on SIFT local descriptors. First, we briefly recall the SIFT algorithm and propose a straightforward way to use it in order to find a few candidate frames which are similar to the query image. Then, we propose an improvement of this method by focusing on the moving regions to filter out the irrelevant features. Finally, we formulate a different way to detect the right candidate using a geometric approach that accounts for the relative position of the points of interest

Detection of an Image in a Video Sequence

The purpose of this work is to discuss possible methods to retrieve a frame in a source video sequence starting from a given image query (for example, a screen shot of a movie scene). In order to do that, we develop and test different approaches that rely on SIFT local descriptors. First, we briefly recall the SIFT algorithm and propose a straightforward way to use it in order to find a few candidate frames which are similar to the query image. Then, we propose an improvement of this method by focusing on the moving regions to filter out the irrelevant features. Finally, we formulate a different way to detect the right candidate using a geometric approach that accounts for the relative position of the points of interest

A mixture model for the dynamic of the gut mucus layer

We introduce a mixture model intended to describe the dynamics of the mucus layer that wraps the gut mucosa. This model takes into account the fluid mechanics of the gut content, the inhomogeneous rheology that depends on the fluid composition, and the main physiological mechanisms that ensure the homoeostasis of the mucus layer. Numerical simulations, based on a finite volume approach, prove the ability of the model to produce a stable steady-state mucus layer. We also perform a sensitivity analysis by using a meta-model based on polynomial chaos in order to identify the main parameters impacting the shape of the mucus layer. The effect of the interaction of the mucus with a population of bacteria is eventually discussed