This study introduces an adaptive three-dimensional (3D) image synthesis technique for creating variational realizations of fibrous meniscal tissue microstructures. The method allows controlled deviation from original geometries by modifying parameters such as porosity, pore size and specific surface area of image patches. The unbiased reconstructed samples matched the morphological and hydraulic properties of original tissues, with relative errors generally below 10%. Additional samples were generated with predefined deviations to increase dataset diversity. Analysis of 1500 synthesized geometries revealed relationships between microstructural features, hydraulic permeability and mechanical properties. Empirical correlations were derived to predict longitudinal and transverse hydraulic permeability as functions of porosity, with values of 0.98 and 0.97, respectively. Finite-element simulations examined mechanical behaviour under compression, showing stress concentrations at fibre cross-links and permeability reductions that varied with porosity and flow direction. These results led to a porosity-dependent model for normalized Young's modulus ([Formula: see text]). The proposed correlations and data augmentation technique aid in investigating structure-property relationships in meniscal tissue, potentially benefiting biomimetic implant design. This approach may help bridge data gaps where obtaining numerous real samples is impractical or unethical.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.
