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INTRODUCTION: The mechanisms of lung inflation and deflation are only partially known. Ventilatory strategies to support lung function rely upon the idea that lung alveoli are isotropic balloons that progressively inflate or deflate and that lung pressure/volume curves derive only by the interplay of critical opening pressures, critical closing pressures, lung history, and position of alveoli inside the lung. This notion has been recently challenged by subpleural microscopy, magnetic resonance, and computed tomography (CT). Phase-contrast synchrotron radiation CT (PC-SRCT) can yield in vivo images at resolutions higher than conventional CT. OBJECTIVES: We aimed to assess the numerosity (ASden) and the extension of the surface of airspaces (ASext) in healthy conditions at different volumes, during stepwise lung deflation, in concentric regions of the lung. METHODS: The study was conducted in seven anesthetized New Zealand rabbits. They underwent PC-SRCT scans (resolution of 47.7 µm) of the lung at five decreasing positive end expiratory pressure (PEEP) levels of 12, 9, 6, 3, and 0 cmH2O during end-expiratory holds. Three concentric regions of interest (ROIs) of the lung were studied: subpleural, mantellar, and core. The images were enhanced by phase contrast algorithms. ASden and ASext were computed by using the Image Processing Toolbox for MatLab. Statistical tests were used to assess any significant difference determined by PEEP or ROI on ASden and ASext. RESULTS: When reducing PEEP, in each ROI the ASden significantly decreased. Conversely, ASext variation was not significant except for the core ROI. In the latter, the angular coefficient of the regression line was significantly low. CONCLUSION: The main mechanism behind the decrease in lung volume at PEEP reduction is derecruitment. In our study involving lung regions laying on isogravitational planes and thus equally influenced by gravitational forces, airspace numerosity and extension of surface depend on the local mechanical properties of the lung.
RESUMO
Accurate and reliable assessment of bone quality requires predictive methods which could probe bone microstructure and provide information on bone mechanical properties. Multiscale modelling and simulation represent a fast and powerful way to predict bone mechanical properties based on experimental information on bone microstructure as obtained through X-ray-based methods. However, technical limitations of experimental devices used to inspect bone microstructure may produce blurry data, especially in in vivo conditions. Uncertainties affecting the experimental data (input) may question the reliability of the results predicted by the model (output). Since input data are uncertain, deterministic approaches are limited and new modelling paradigms are required. In this paper, a novel stochastic multiscale model is developed to estimate the elastic properties of bone while taking into account uncertainties on bone composition. Effective elastic properties of cortical bone tissue were computed using a multiscale model based on continuum micromechanics. Volume fractions of bone components (collagen, mineral, and water) were considered as random variables whose probabilistic description was built using the maximum entropy principle. The relevance of this approach was proved by analysing a human bone sample taken from the inferior femoral neck. The sample was imaged using synchrotron radiation micro-computed tomography. 3-D distributions of Haversian porosity and tissue mineral density extracted from these images supplied the experimental information needed to build the stochastic models of the volume fractions. Thus, the stochastic multiscale model provided reliable statistical information (such as mean values and confidence intervals) on bone elastic properties at the tissue scale. Moreover, the existence of a simpler "nominal model", accounting for the main features of the stochastic model, was investigated. It was shown that such a model does exist, and its relevance was discussed.