Free-breathing myocardial T2 measurements at 1.5T

POSTER PRESENTATIONInternational audienc

Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms

In a preregistered, cross-sectional study we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC=0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable

The global abundance of tree palms

Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests

Mise en oeuvre d'un système de reconstruction adaptif pour l'IRM 3D des organes en mouvement

Magnetic Resonance Imaging (MRI) has two main features. The first one, its ability to manipulate contrast, is a major advantage compared to the other imaging modalities. It allows to access complementary information for a better detectability and a diagnostic more accurate. This is especially useful for myocardium pathologies. The second feature of MRI is also one of its main drawbacks: the acquisition process is slow. Therefore, patient motion is a significant obstacle because it disturbs the acquisition process, which leads to artifacts in the reconstructed image. Cardiac and thoracic imaging are particularly sensitive to this motion issue. The aim of this thesis is to develop a new motion correction method that can be integrated in a multi-contrast workflow. In a first phase, we studied apart the motion correction problem. To do so, we focused more particularly on the GRICS method which was already developed in the IADI laboratory. This method allows the joint reconstruction of an image free from artifact and a non-rigid motion model that describes the displacements occurring during the acquisition. The first major contribution of this thesis is an improvement of the GRICS method consisting mainly in adapting it to the 3D imaging. This was achieved with a new adaptive regularization method that perfectly suits the inverse problem posed in GRICS. The second major contribution of this thesis consists in the simultaneous management of the motion correction on multiple acquisitions with different contrasts. To do so, the MRI examination is considered as a whole. Thus we make the most of information shared between the different contrasts. All these methods have been applied and validated by simulations, tests on phantom, on healthy volunteers and on patients as part of clinical studies. We aimed more particularly at cardiac MR. Finally the developed methods improve the acquisition and reconstruction workflow in the framework of a real clinical routineL'Imagerie par Résonance Magnétique (IRM) présente deux caractéristiques principales. La première, sa capacité à manipuler le contraste, constitue son principal avantage par rapport aux autres modalités d'imagerie. Cela permet d'obtenir des informations complémentaires pour une meilleure détectabilité et une meilleure précision dans le diagnostic. Cela est particulièrement appréciable pour les pathologies du myocarde. La seconde caractéristique de l'IRM est également l'un de ces principaux inconvénients : le processus d'acquisition est relativement lent. De ce fait, les mouvements du patient constituent un obstacle important puisqu'ils perturbent ce processus d'acquisition, ce qui se traduit par des artéfacts dans l'image reconstruite. L'imagerie cardiaque et abdominale sont donc particulièrement sensibles à cette problématique du mouvement. L'objectif de cette thèse est donc de proposer une méthode de correction de mouvement intégrable dans un contexte multi-contraste. Nous avons étudié dans un premier temps la question de la correction de mouvement seule. Pour cela, nous nous sommes plus particulièrement intéressés à la méthode GRICS déjà développée au laboratoire IADI. Cette méthode permet la reconstruction conjointe d'une image sans artéfact et d'un modèle de mouvement non rigide permettant de corriger les déplacements qui surviennent pendant l'acquisition. Le premier apport majeur de cette thèse a consisté à améliorer la méthode GRICS, notamment pour l'adapter à l'imagerie volumique 3D. Il s'agit d'une nouvelle méthode de régularisation adaptative particulièrement adaptée au problème inverse posé dans GRICS. Le second apport majeur de cette thèse a consisté à gérer la correction de mouvement GRICS de manière conjointe sur des acquisitions présentant des contrastes différents. Il s'agit de concevoir l'examen IRM comme un tout et d'exploiter au mieux les informations partagées entre les différents contrastes. Toutes ces méthodes ont été appliquées et validées par des simulations, des tests sur fantôme, sur volontaires sains et sur des patients dans la cadre d'études cliniques. L'application cardiaque a été particulièrement visée. Les méthodes développées ont permis d'améliorer le processus d'acquisition et de reconstruction dans le contexte clinique rée

Implementation of an adaptive reconstruction system for 3D MRI of moving organs

L'Imagerie par Résonance Magnétique (IRM) présente deux caractéristiques principales. La première, sa capacité à manipuler le contraste, constitue son principal avantage par rapport aux autres modalités d'imagerie. Cela permet d'obtenir des informations complémentaires pour une meilleure détectabilité et une meilleure précision dans le diagnostic. Cela est particulièrement appréciable pour les pathologies du myocarde. La seconde caractéristique de l'IRM est également l'un de ces principaux inconvénients : le processus d'acquisition est relativement lent. De ce fait, les mouvements du patient constituent un obstacle important puisqu'ils perturbent ce processus d'acquisition, ce qui se traduit par des artéfacts dans l'image reconstruite. L'imagerie cardiaque et abdominale sont donc particulièrement sensibles à cette problématique du mouvement. L'objectif de cette thèse est donc de proposer une méthode de correction de mouvement intégrable dans un contexte multi-contraste. Nous avons étudié dans un premier temps la question de la correction de mouvement seule. Pour cela, nous nous sommes plus particulièrement intéressés à la méthode GRICS déjà développée au laboratoire IADI. Cette méthode permet la reconstruction conjointe d'une image sans artéfact et d'un modèle de mouvement non rigide permettant de corriger les déplacements qui surviennent pendant l'acquisition. Le premier apport majeur de cette thèse a consisté à améliorer la méthode GRICS, notamment pour l'adapter à l'imagerie volumique 3D. Il s'agit d'une nouvelle méthode de régularisation adaptative particulièrement adaptée au problème inverse posé dans GRICS. Le second apport majeur de cette thèse a consisté à gérer la correction de mouvement GRICS de manière conjointe sur des acquisitions présentant des contrastes différents. Il s'agit de concevoir l'examen IRM comme un tout et d'exploiter au mieux les informations partagées entre les différents contrastes. Toutes ces méthodes ont été appliquées et validées par des simulations, des tests sur fantôme, sur volontaires sains et sur des patients dans la cadre d'études cliniques. L'application cardiaque a été particulièrement visée. Les méthodes développées ont permis d'améliorer le processus d'acquisition et de reconstruction dans le contexte clinique réelMagnetic Resonance Imaging (MRI) has two main features. The first one, its ability to manipulate contrast, is a major advantage compared to the other imaging modalities. It allows to access complementary information for a better detectability and a diagnostic more accurate. This is especially useful for myocardium pathologies. The second feature of MRI is also one of its main drawbacks: the acquisition process is slow. Therefore, patient motion is a significant obstacle because it disturbs the acquisition process, which leads to artifacts in the reconstructed image. Cardiac and thoracic imaging are particularly sensitive to this motion issue. The aim of this thesis is to develop a new motion correction method that can be integrated in a multi-contrast workflow. In a first phase, we studied apart the motion correction problem. To do so, we focused more particularly on the GRICS method which was already developed in the IADI laboratory. This method allows the joint reconstruction of an image free from artifact and a non-rigid motion model that describes the displacements occurring during the acquisition. The first major contribution of this thesis is an improvement of the GRICS method consisting mainly in adapting it to the 3D imaging. This was achieved with a new adaptive regularization method that perfectly suits the inverse problem posed in GRICS. The second major contribution of this thesis consists in the simultaneous management of the motion correction on multiple acquisitions with different contrasts. To do so, the MRI examination is considered as a whole. Thus we make the most of information shared between the different contrasts. All these methods have been applied and validated by simulations, tests on phantom, on healthy volunteers and on patients as part of clinical studies. We aimed more particularly at cardiac MR. Finally the developed methods improve the acquisition and reconstruction workflow in the framework of a real clinical routin

« D’une quintuple infoliature mobile » (introduction au dossier « Relire le Ve livre »)

International audienceIntroduction to the proceedings of the study day devoted to a rereading of The Fifth Book. Overview of and new insights from research on the posthumous book, forty years after the publication of Mireille Huchon’s /Rabelais grammairien/ in 1981, a seminal moment in the history of Rabelaisian studies.Introduction aux actes de la journée d’étude consacrée à la relecture du « Ve livre ». État des lieux et nouvelles perspectives de la recherche sur le livre posthume, quarante ans après le /Rabelais grammairien/ de Mireille Huchon, dont la parution en 1981 est à marquer d’une croix blanche dans l’histoire des études rabelaisiennes

GPU Implementation of Levenberg-Marquardt Optimization for T1 Mapping

International audienceT 1 mapping is an emerging MRI research tool to characterize diseased myocardial tissue. The T i map is generated by fitting an exponential relaxation curve to the acquired image data. Levenberg-Marquardt algorithm is a standard way to solve this nonlinear curve fitting problem. However, the execution on the standard CPU can be time-consuming and incompatible with clinical routine. In this paper, a GPU implementation is performed to reduce the computation time of the standard T 1 mapping. In addition, a new vectorized approach is proposed to include spatial regularization in the curve fitting process to improve the robustness. The GPU implementation is validated on NVIDIA K42000 GPU using cardiac T1 data from 16 volunteers. The computation time shows significant decrease in both pixel-wise and vectorized curve fitting. The pixel-wise curve fitting is accelerated by a factor of 30+ compared to the standard sequential C code and the vectorized curve fitting is improved by a factor of 47 and 38 for 3-parameter and 2-parameter curve fitting compared to the Matlab code

A Modeling Tentative of Operation of Gas Sensor through Atomic Scale Insights

International audienc

Narrations fabuleuses. Mélanges en l’honneur de Mireille Huchon

International audienc