Computational Lung Biomechanics: Many-scale Modeling and Estimation – LungManyScale

The project uses a quasi-static poromechanical framework based on Biot's macroscopic theory, adapted to large deformations, modeling the lung as a biphasic (solid/air) medium. The research is structured into two Work Packages (WP1 and WP2).

WP1: Multiscale Modeling of Pulmonary Poromechanics.

This WP develops coupled modeling across multiple scales, from the alveolus to the organ. The objective is, in particular, to bridge the gap between mechanical responses measured at the tissue and organ scales.

WP2: Diagnostic & Prognostic Tools for IPF.

This WP uses the multiscale model in close interaction with clinical data, including thoracic CT images (inspiration/expiration).

The estimation of microscopic parameters (e.g., stiffness) and states (e.g., stress) is performed by solving an inverse problem.

The work stemming from this project led to the development of the model, resulting in several key findings.

Personalization and Regional Compliance: A poromechanical model of the lung was developed, which can be personalized using clinical CT images to estimate regional lung compliance in IPF and COVID-19 patients.

The estimation of regional stiffness parameters is consistent with current knowledge of the disease, showing that the fibrotic region is significantly stiffer than the healthy region. To improve robustness, a new parameterization for the inverse problem was introduced, allowing for the estimation of a personalized pleural pressure in addition to material parameters.

Microporomechanical Modeling: A general microporomechanical framework for large deformations, including surface tension, was developed for the lung parenchyma.

This model, which provides a micromechanical foundation for macroscopic models, allows for the study of the influence of microscopic characteristics (morphology and stiffness of the alveolar walls) on the macroscopic response. In particular, it reproduced the hysteresis of the pressure-volume response by modeling a surface tension dependent on the area.

Gravity Integration: The integration of gravity into the macroscopic model, often neglected, was achieved by proposing new heterogeneous pleural pressure boundary conditions to maintain overall equilibrium.

The results confirm that gravity influences the pulmonary response, inducing deformation and stress heterogeneities consistent with in vivo data.

Methodological Advances: The personalization pipeline was automated.

Work focused on improving estimation methods, including a new formulation of the large-strain equilibrium deviation method (EGM) for the direct identification of parameters.

Finally, an inverse uncertainty pipeline was established to quantify the robustness of the estimation of key parameters (stiffness and pleural pressure) in the face of noise and model errors, which is essential for the reliability of digital twins.

The project aims to transform the model into a truly reliable clinical digital twin.

At the model level, the goal is to improve the description of air and blood flow and gas exchange, and to incorporate the remodeling timescale and associated mechanisms.

At the computational level, a model reduction method will be developed to allow for customization and real-time evaluation of the model in a clinical setting.

Long-Term Clinical Impact: The prognostic model will be applied to longitudinal data to study disease progression and the mechanical impact of medications, enabling statistical quantification of the role of mechanics in the progression of IPF.

The model will also be applied to other diseases, including vascular lung diseases, particularly pulmonary arterial hypertension.

The lungs are the primary organs of the respiratory system in humans and many animals, responsible for molecular exchanges between external air and internal blood through mechanical ventilation. It has an extraordinary complex architecture, with the inherent fractal structure of the bronchial and blood vessel trees, as well as the hierarchical structure of the parenchyma. Lung biomechanics has been extensively studied by physiologists, experimentally as well as theoretically, from the air flow, blood flow and tissue stress points of view, laying the ground for our current fundamental understanding of the relationship between function and mechanical behavior. However, many questions remain, notably in the intricate coupling between the multiple constituents, between the many phenomena taking place at different spatial and temporal scales in health and disease. For example, even for healthy lungs, there is no quantitative model allowing to link tissue-level and organ-level experimental material responses.

These fundamental questions represent real clinical challenges, as pulmonary diseases are an important health burden. Interstitial lung diseases, for instance, affect several million people globally. Idiopathic Pulmonary Fibrosis, notably, a progressive form of interstitial lung diseases where some alveolar septa get thicker and stiffer while others get completely damaged, remains poorly understood, poorly diagnosed, and poorly treated, with a current median survival rate inferior to 5 years. It has, however, been hypothesized that a mechanical vicious cycle is in place within the parenchyma of IPF patients, where fibrosis and damage induce large stresses, which in turns favor fibrosis.

The general goal of this project is twofold: (i) scientifically, to better understand pulmonary (solid) mechanics, from the alveolar scale to the organ in health and (IPF) disease; (ii) clinically, to improve diagnosis and prognosis of (IPF) patients through personalized computational modeling. More precisely, I propose to develop a many-scale model of the parenchymal biomechanics, at all relevant spatial scales from the alveolus to the organ, and at the temporal scales of the breathing cycle and fibrosis process. Different representations at successive spatial scales will be linked by a computational nonlinear homogenization strategy with a priori model reduction based on a neural network. The model will integrate the rather unique experimental data produced by Drs. Bel-Brunon and Trunfio-Sfarghiu from LaMCoS (INSA-Lyon), i.e., 30 microtomography images at alveolar scale, plus 10 inflation tests of lobules: microstructures will be extracted from the images and systematically analyzed, and model parameters will be estimated from the mechanical tests. The model will also integrate clinical-radiological data provided by Profs. Nunes and Brillet from Avicenne APHP Hospital, i.e., standard pulmonary function tests and thoracic computed tomography imaging on 10 IPF patients plus 5 normal lung controls: a pipeline to estimate observable model parameters from clinical data will be set up, and generic values will be defined for the remaining parameters. The model and estimation procedure will represent augmented diagnosis and prognosis tools for the clinicians.

The project will be coordinated by Dr. Genet, who is currently an Assistant Professor in the Mechanics Department of École Polytechnique with research posting within the M?DISIM team, which belongs to both INRIA and the Solid Mechanics Laboratory of École Polytechnique/CNRS. Throughout the project he will be assisted by Drs. Chapelle and Moireau at INRIA/École Polytechnique, and maintain strong scientific collaborations with the LaMCoS at INSA-Lyon and Télécom-SudParis, as well as strong clinical collaborations with the Avicenne APHP Hospital and Hypoxia & Lung Laboratory of Paris XIII University/INSERM.