Image-Based Quantification and Coupled PNM-FEM of Architecture, Deformation, and Fluid Dynamics in Biological Porous Media Under Mechanical Loading
The present work describes an image-based method that integrates Pore Network Modeling (PNM), Finite Element Method (FEM), and statistical image analysis to study the involvement of fluid on the mechanical responses of load-bearing connective tissues. The extracellular matrix (ECM), a porous scaffold filled with fluid, governs key cellular functions. The composition and architectural characteristics of the ECM, such as porosity, pore connectivity, pore size, and tortuosity, vary with tissue type and states, and these parameters influence the mechanical properties, like stiffness and permeability. Load-bearing soft tissues, such as the meniscus, display multilayered ECM architectures with graded mechanical properties requisite for function. These are architectural and mechanical traits that require a coherent framework to express, quantify, and iterate over. The present study investigates the ECM of meniscal tissue using micro-CT scans, tortuosity, Minkowski Functionals (MFs), as well as permeability modelling based on PNM and FEM. We quantify porosity, pore connectivity, pore size, throat size and length, permeability variation along the sample, permeability distribution, tortuosity-distributions, and strain-dependent permeability, which demonstrate significant, spatially heterogeneous reductions in permeability by up to 35% at 15% strain. This integrated approach—combining image analysis tools such as tortuosity and MF in addition to PNM-FEM coupling— can effectively quantify the architectural and fluid flow parameters of human tissues under loading. Compared to other numerical methods, coupling FEM and PNM is computationally efficient for evaluating changes in the internal structure of a porous medium and pressure drop influences on permeability.
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