Investigation of the association between central arterial stiffness and aggregate g-ratio in cognitively unimpaired adults

Stiffness of the large arteries has been shown to impact cerebral white matter (WM) microstructure in both younger and older adults. However, no study has yet demonstrated an association between arterial stiffness and aggregate g-ratio, a specific magnetic resonance imaging (MRI) measure of axonal myelination that is highly correlated with neuronal signal conduction speed. In a cohort of 38 well-documented cognitively unimpaired adults spanning a wide age range, we investigated the association between central arterial stiffness, measured using pulse wave velocity (PWV), and aggregate g-ratio, measured using our recent advanced quantitative MRI methodology, in several cerebral WM structures. After adjusting for age, sex, smoking status, and systolic blood pressure, our results indicate that higher PWV values, that is, elevated arterial stiffness, were associated with lower aggregate g-ratio values, that is, lower microstructural integrity of WM. Compared to other brain regions, these associations were stronger and highly significant in the splenium of the corpus callosum and the internal capsules, which have been consistently documented as very sensitive to elevated arterial stiffness. Moreover, our detailed analysis indicates that these associations were mainly driven by differences in myelination, measured using myelin volume fraction, rather than axonal density, measured using axonal volume fraction. Our findings suggest that arterial stiffness is associated with myelin degeneration, and encourages further longitudinal studies in larger study cohorts. Controlling arterial stiffness may represent a therapeutic target in maintaining the health of WM tissue in cerebral normative aging

Local characterization of deformations and water transfers in meat during thermal constraints by NMR imaging

La cuisson est un procédé universel de transformation de la matière première carnée en aliment. Le chauffage de la matrice musculaire conditionne diverses qualités organoleptiques, technologiques, sanitaires et nutritionnelles des viandes cuites. La cuisson étant appliquée de plus en plus fréquemment en conditions industrielles standardisées, il est pertinent de modéliser certains mécanismes clés dans le déterminisme de ces qualités afin d’optimiser le procédé. Pour cela, une démarche expérimentale originale a été mise en place fondée sur une analyse quantitative, locale, dynamique et in situ de la viande pendant la cuisson. Cette démarche ne fait pas d’hypothèse réductionniste en étudiant un échantillon intact à l’échelle de l’aliment consommé, ni d’hypothèse simplificatrice en prenant en compte les variations spatiales de la température dans l’échantillon. Elle s’appuie sur des développements originaux en imagerie par résonance magnétique nucléaire à haut champ et en traitement d’images pour cartographier la déformation et la quantité d’eau. Des modèles robustes liant température, déformation et quantité d’eau ont été obtenus pour des muscles de teneur variable en tissu conjonctif. Les résultats montrent principalement une augmentation de la déformation avec la température en plusieurs phases dont les caractéristiques dépendent de la composition du muscle, et une diminution de la quantité d’eau avec la température. Tous ces résultats sont discutés et interprétés au regard du comportement à la température des différents composants du muscle. Ce travail montre d’abord que l’imagerie dynamique, quantitative et multiparamétrique permet de décrypter des mécanismes intervenant lors de la cuisson des viandes sans établir des hypothèses réductrices lors de l’interprétation de ces phénomènes. Elle a conduit de plus, à des développements méthodologiques applicables à d’autres champs et ouvre la voie à d’autres investigations dans le domaine de l’optimisation qualitative des produits carnés transformés.Cooking is a general process which transforms the meat raw material into food. The heating of muscle matrix influences different organoleptic, industrial, health and nutritional qualities of cooked meat. Cooking being applied more and more frequently in standardized and industrial conditions, it makes sense to model some key mechanisms which determine the latter qualities in order to optimize the process. For this purpose, an original experimental approach has been developed based on a quantitative, local, dynamic and in situ analysis of the meat during cooking. This approach is not based on any reductionist hypothesis by studying an intact sample at the scale of the consumed food, nor by the simplifying assumption of taking into account the spatial variations of temperature in the sample. It is based on original developments in nuclear magnetic resonance imaging at high-field and on image processing in order to map deformation and the water content. Robust models linking temperature, deformation and water content were obtained for muscles differing from their content in connective tissue. The results mainly show a deformation increase with the temperature in several phases whose characteristics depend on the muscle composition, and a decrease in the water content with temperature. All these results are discussed and interpreted thanks to the temperature behavior of the various muscle components. This work first shows that quantitative, multi-parametric and dynamic imaging can decipher the mechanisms involved during meat cooking, without formulating simplifying assumptions in the interpretation of these phenomena. Furthermore, it has led to methodological developments applicable to other fields and paves the way for further investigations in the field of quality optimization of processed meat products

Caractérisation locale des déformations et des transferts de matière dans le muscle sous contraintes thermiques par imagerie RMN

Cooking is a general process which transforms the meat raw material into food. The heating of muscle matrix influences different organoleptic, industrial, health and nutritional qualities of cooked meat. Cooking being applied more and more frequently in standardized and industrial conditions, it makes sense to model some key mechanisms which determine the latter qualities in order to optimize the process. For this purpose, an original experimental approach has been developed based on a quantitative, local, dynamic and in situ analysis of the meat during cooking. This approach is not based on any reductionist hypothesis by studying an intact sample at the scale of the consumed food, nor by the simplifying assumption of taking into account the spatial variations of temperature in the sample. It is based on original developments in nuclear magnetic resonance imaging at high-field and on image processing in order to map deformation and the water content. Robust models linking temperature, deformation and water content were obtained for muscles differing from their content in connective tissue. The results mainly show a deformation increase with the temperature in several phases whose characteristics depend on the muscle composition, and a decrease in the water content with temperature. All these results are discussed and interpreted thanks to the temperature behavior of the various muscle components. This work first shows that quantitative, multi-parametric and dynamic imaging can decipher the mechanisms involved during meat cooking, without formulating simplifying assumptions in the interpretation of these phenomena. Furthermore, it has led to methodological developments applicable to other fields and paves the way for further investigations in the field of quality optimization of processed meat products.La cuisson est un procédé universel de transformation de la matière première carnée en aliment. Le chauffage de la matrice musculaire conditionne diverses qualités organoleptiques, technologiques, sanitaires et nutritionnelles des viandes cuites. La cuisson étant appliquée de plus en plus fréquemment en conditions industrielles standardisées, il est pertinent de modéliser certains mécanismes clés dans le déterminisme de ces qualités afin d’optimiser le procédé. Pour cela, une démarche expérimentale originale a été mise en place fondée sur une analyse quantitative, locale, dynamique et in situ de la viande pendant la cuisson. Cette démarche ne fait pas d’hypothèse réductionniste en étudiant un échantillon intact à l’échelle de l’aliment consommé, ni d’hypothèse simplificatrice en prenant en compte les variations spatiales de la température dans l’échantillon. Elle s’appuie sur des développements originaux en imagerie par résonance magnétique nucléaire à haut champ et en traitement d’images pour cartographier la déformation et la quantité d’eau. Des modèles robustes liant température, déformation et quantité d’eau ont été obtenus pour des muscles de teneur variable en tissu conjonctif. Les résultats montrent principalement une augmentation de la déformation avec la température en plusieurs phases dont les caractéristiques dépendent de la composition du muscle, et une diminution de la quantité d’eau avec la température. Tous ces résultats sont discutés et interprétés au regard du comportement à la température des différents composants du muscle. Ce travail montre d’abord que l’imagerie dynamique, quantitative et multiparamétrique permet de décrypter des mécanismes intervenant lors de la cuisson des viandes sans établir des hypothèses réductrices lors de l’interprétation de ces phénomènes. Elle a conduit de plus, à des développements méthodologiques applicables à d’autres champs et ouvre la voie à d’autres investigations dans le domaine de l’optimisation qualitative des produits carnés transformés

Parsimonious discretization for characterizing multi‐exponential decay in magnetic resonance

International audienceWe address the problem of analyzing noise-corrupted magnetic resonance transverse decay signals as a superposition of underlying independently decaying monoexponentials of positive amplitude. First, we indicate the manner in which this is an ill-conditioned inverse problem, rendering the analysis unstable with respect to noise. Second, we define an approach to this analysis, stabilized solely by the nonnegativity constraint without regularization. This is made possible by appropriate discretization, which is coarser than that often used in practice. Thirdly, we indicate further stabilization by inspecting the plateaus of cumulative distributions. We demonstrate our approach through analysis of simulated myelin water fraction measurements, and compare the accuracy with more conventional approaches. Finally, we apply our method to brain imaging data obtained from a human subject, showing that our approach leads to maps of the myelin water fraction which are much more stable with respect to increasing noise than those obtained with conventional approaches

The NESMA Filter: Nonlocal Estimation of Multi-Spectral Magnitudes for Noise Reduction in Magnetic Resonance Images

International audienc

MUTANT : a new image filtering paradigm for improved parameters determination. Application to myelin water fraction mapping and sodium concentrations imaging

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Enhanced quality of myelin water fraction mapping from GRASE imaging data of human brain using a new nonlocal estimation of multi-spectral magnitudes (NESMA) filter

Changes in myelin water fraction (MWF) represent a biomarker for central nervous system disease. However, high quality mapping of MWF is challenging, requiring very high signal-to-noise ratio for accurate and stable results. In this work, we demonstrate the potential of a new multispectral filter to permit high quality MWF mapping using in-vivo GRASE brain imaging datasets. Indeed, unlike conventional averaging filters, our filter permits substantial reduction of the random variation in derived MWF estimates while preserving edges and small structures. Finally, our results regarding patterns of MWF as a function of age are consistent with recent literature