Calculated Ventilation and Effort Distribution as a Measure Of Respiratory Disease and Heliox Effectiveness

International audienceIn spite of numerous clinical studies, there is no consensus on the benefit Heliox mixtures can bring to asthmatic patients in terms of work of breathing and ventilation distribution. In this article we use a 3D finite element mathematical model of the lung to study the impact of asthma on effort and ventilation distribution along with the effect of Heliox compared to air. Lung surface displacement fields extracted from computed tomography medical images are used to prescribe realistic boundary conditions to the model. Asthma is simulated by imposing bronchoconstrictions to some airways of the tracheo-bronchial tree based on statistical laws deduced from the literature. This study illuminates potential mechanisms for patient responsiveness to Heliox when affected by obstructive pulmonary diseases. Responsiveness appears to be function of the pathology severity, as well as its distal position in the tracheo-bronchial tree and geometrical position within the lung

Modélisation multi-échelle de la ventilation pulmonaire dans des cas sains et pathologiques

The lungs contain a tree through which the air flows. It supplies a porous region, the parenchyma, where gas exchanges with blood take place. Some pathologies affect the tree structure or the parenchyma integrity. They can induce ventilation defects or increased respiratory efforts. In vivo-studies are complex and mathematical modeling can provide some insights on the lung behavior, the pathologies’ impacts or the efficiency of treatments.In the first part of this thesis, we propose a ventilation model of the lung based on a mechanical description. A 0D tree is strongly coupled to a 3D parenchyma model. We show the influence of chosen boundary conditions as well as tree or parenchyma alterations on the ventilation distribution. Results are compared with those provided by a simpler model, often used in the literature.In a second part, we use the tree-parenchyma coupled model to investigate how breathing gas mixtures less dense than air would potentially reduce efforts and ensure a better ventilation. To that end, we build an asthmatic tree model.In the next part, we develop an approach to get insights on severe constrictions distribution based on the analysis of dynamic lung ventilation images. To do so, the coupled ventilation model is used along with a machine learning technique.Finally, two prospective works are presented. First, we propose extensions to the ventilation models introduced in the first part as a step towards spriometry modeling. The last study is part of a global perspective that aims at getting insights on the lung geometry based on simple measurements on the patient’s body.Les poumons sont constitués d’un arbre par lequel circule l’air et qui alimente le parenchyme où ont lieu les échanges gazeux avec le sang. Certaines pathologies affectent la structure de l’arbre ou du parenchyme induisant des défauts dans l’approvisionnement en air ou des efforts respiratoires accrus. Etudier l’organe in-vivo est complexe. La modélisation mathématique peut apporter un éclairage utile sur les effets associés aux pathologies touchant le poumon, et la pertinence des traitements proposés. Dans la première partie de cette thèse, nous proposons un modèle mécanique de ventilation pulmonaire. Un arbre 0D est couplé de manière forte à un modèle de parenchyme 3D. On met en évidence l’impact sur la distribution de ventilation des conditions aux limites et d’altérations de l’arbre ou du parenchyme. Le comportement de ce modèle est comparé à celui d’un modèle plus simple et couramment utilisé.Dans une deuxième partie, on propose un modèle d’arbre asthmatique et on étudie dans quelle mesure respirer un gaz moins dense que l’air permet de diminuer les efforts et les défauts de ventilation. On propose ensuite une approche visant à déterminer la distribution des constrictions bronchiques les plus sévères à partir de données d’imagerie. Notre démarche s’appuie sur l’utilisation du modèle de ventilation, enrichie par une technique d’apprentissage statistique.On présente finalement deux études prospectives. La première étend les modèles de ventilation introduits précédemment avec pour objectif de modéliser la spirométrie. La deuxième s’inscrit dans une perspective visant à déterminer la géométrie du poumon à partir de mesures simples prises sur le corps du patient

Shape optimization of a sodium cooled fast reactor

Traditional designs of sodium cooled fast reactors have a positive sodium expansion feedback. During a loss of flow transient without scram, sodium heating and boiling thus insert a positive reactivity and prevents the power from decreasing. Recent studies led at CEA, AREVA and EDF show that cores with complex geometries can feature a very low or even a negative sodium void worth.(1, 2) Usual optimization methods for core conception are based on a parametric description of a given core design(3).(4) New core concepts and shapes can then only be found by hand. Shape optimization methods have proven very efficient in the conception of optimal structures under thermal or mechanical constraints.(5, 6) First studies show that these methods could be applied to sodium cooled core conception.(7) In this paper, a shape optimization method is applied to the conception of a sodium cooled fast reactor core with low sodium void worth. An objective function to be minimized is defined. It includes the reactivity change induced by a 1% sodium density decrease. The optimization variable is a displacement field changing the core geometry from one shape to another. Additionally, a parametric optimization of the plutonium content distribution of the core is made, so as to ensure that the core is kept critical, and that the power shape is flat enough. The final shape obtained must then be adjusted to a get realistic core layout. Its caracteristics can be checked with reference neutronic codes such as ERANOS. Thanks to this method, new shapes of reactor cores could be inferred, and lead to new design ideas

Predicted airway obstruction distribution based on dynamical lung ventilation data: a coupled modeling-machine learning methodology

International audienceIn asthma and COPD, some airways of the tracheo-bronchial tree can be constricted, from moderate narrowing up to closure. Those pathological patterns affect the lung ventilation distribution. While some imaging techniques enable visualization and quantification of constrictions in proximal generations, no non-invasive technique provides precise insights on what happens in more distal areas. In this work, we propose a process that exploits lung ventilation measures to access positions of airways closures in the tree. This identification approach combines the lung ventilation model in which a tree is strongly coupled to a parenchyma description along with a machine learning approach. Based on synthetic data generated with typical temporal and spatial resolutions as well as reconstruction errors, we obtain very encouraging results with a detection rate higher than 90%

Multicenter automatic detection of invasive carcinoma on breast whole slide images.

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice