Browsing by Author "Aviles-Rojas, Nibaldo"

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    Multiscale modeling of lung mechanics: From alveolar microstructure to pulmonary function
    (2023) Hurtado, Daniel E.; Aviles-Rojas, Nibaldo; Concha, Felipe
    The mechanical behavior of the lungs has long been associated with the structural properties of alveoli in pulmonary medicine. However, this structure-function relationship has mostly been qualitative, as experimentation in real lungs is costly and limited by ethical standards. Here we present a poromechanical multiscale model that connects key alveolar features with organ-level function. To this end, we first revisit an asymptotic homogenization framework for finite-deformation poromechanics and formulate fine-scale and coarse-scale problems that govern lung mechanics. We further inform the coarse-scale problem using a tetrakaidecahedron micromechanical model for the alveolar response at the fine scale that strongly depends on the alveolar-wall elastic modulus and the initial alveolar porosity. Based on this formulation, we construct a non-linear finite element model from anatomical geometries to simulate the response of human lungs connected to a mechanical ventilator under pressure-controlled and volume-controlled protocols. We show that the predicted signals for airway pressure, airway flow, and lung volume capture the dynamic waveform characteristics observed in human lungs. Further, we demonstrate that lung behavior, measured in terms of respiratory-system compliance, strongly depends on the alveolar-wall elasticity and alveolar porosity. In particular, we show that variations in these microstructural parameters result in marked changes in compliance that follow the structure-function relations observed in lungs with pulmonary fibrosis and emphysema, two prevalent chronic respiratory diseases. We envision that our multiscale lung model can enhance current in silico efforts to experimentation in respiratory research and provide a computational framework for clinically-relevant simulations. Codes are available for download at https://github.com/comp-medicine-uc/multiscale-lung-mechanics.
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    Whole-lung finite-element models for mechanical ventilation and respiratory research applications
    (2022) Aviles-Rojas, Nibaldo; Hurtado, Daniel E.
    Mechanical ventilation has been a vital treatment for Covid-19 patients with respiratory failure. Lungs assisted with mechanical ventilators present a wide variability in their response that strongly depends on air-tissue interactions, which motivates the creation of simulation tools to enhance the design of ventilatory protocols. In this work, we aim to create anatomical computational models of the lungs that predict clinically-relevant respiratory variables. To this end, we formulate a continuum poromechanical framework that seamlessly accounts for the air-tissue interaction in the lung parenchyma. Based on this formulation, we construct anatomical finite-element models of the human lungs from computed-tomography images. We simulate the 3D response of lungs connected to mechanical ventilation, from which we recover physiological parameters of high clinical relevance. In particular, we provide a framework to estimate respiratory-system compliance and resistance from continuum lung dynamic simulations. We further study our computational framework in the simulation of the supersyringe method to construct pressure-volume curves. In addition, we run these simulations using several state-of-the-art lung tissue models to understand how the choice of constitutive models impacts the whole-organ mechanical response. We show that the proposed lung model predicts physiological variables, such as airway pressure, flow and volume, that capture many distinctive features observed in mechanical ventilation and the supersyringe method. We further conclude that some constitutive lung tissue models may not adequately capture the physiological behavior of lungs, as measured in terms of lung respiratory-system compliance. Our findings constitute a proof of concept that finite-element poromechanical models of the lungs can be predictive of clinically-relevant variables in respiratory medicine.