Comparison of optimization parametrizations for regional lung compliance estimation using personalized pulmonary poromechanical modeling.

Interstitial lung diseases, such as idiopathic pulmonary fibrosis (IPF) or post-COVID-19 pulmonary fibrosis, are progressive and severe diseases characterized by an irreversible scarring of interstitial tissues that affects lung function. Despite many efforts, these diseases remain poorly understood and poorly treated. In this paper, we propose an automated method for the estimation of personalized regional lung compliances based on a poromechanical model of the lung. The model is personalized by integrating routine clinical imaging data - namely computed tomography images taken at two breathing levels in order to reproduce the breathing kinematic-notably through an inverse problem with fully personalized boundary conditions that is solved to estimate patient-specific regional lung compliances. A new parametrization of the inverse problem is introduced in this paper, based on the combined estimation of a personalized breathing pressure in addition to material parameters, improving the robustness and consistency of estimation results. The method is applied to three IPF patients and one post-COVID-19 patient. This personalized model could help better understand the role of mechanics in pulmonary remodeling due to fibrosis; moreover, patient-specific regional lung compliances could be used as an objective and quantitative biomarker for improved diagnosis and treatment follow up for various interstitial lung diseases.

Mechanical characterization and identification of material parameters of porcine aortic valve leaflets.

The ideal artificial heart valve does not exist yet. Understanding of mechanical and structural properties of natural tissues is necessary to improve the design of biomimetic aortic valve. Besides these properties are needed for the finite element modeling as input parameters. In this study we propose a new method combining biaxial tests and digital image correlation. These tests are carried out on porcine aortic valves. In this work, we use a modified version of the HGO (Holzapfel-Gasser-Ogden) model which is classically used for hyper-elastic and anisotropic soft tissues. This model can include fiber orientation. The identification of HGO model parameters can be determined using experimental data and two different protocols. One protocol is based on the identification of collagen fibers orientation as well as the mechanical parameters. The second one, is based on a complementary experiment to determine orientation (confocal laser scanning microscope). Both lead to determine different sets of material parameters. We show that the model is more likely to reproduce the actual mechanical behavior of the heart valves in the second case and that a minimum of three different loading conditions for the biaxial tensile tests is required to obtain a relevant set of parameters.