On the characteristics of natural hydraulic dampers: An image-based approach to study the fluid flow behaviour inside the human meniscal tissue

The meniscal tissue is a layered material with varying properties influenced by collagen content and arrangement. Understanding the relationship between structure and properties is crucial for disease management, treatment development, and biomaterial design. The internal layer of the meniscus is softer and more deformable than the outer layers, thanks to interconnected collagen channels that guide fluid flow. To investigate these relationships, we propose a novel approach that combines Computational Fluid Dynamics (CFD) with Image Analysis (CFD-IA). We analyze fluid flow in the internal architecture of the human meniscus across a range of inlet velocities (0.1mm/s to 1.6m/s) using high-resolution 3D micro-computed tomography scans. Statistical correlations are observed between architectural parameters (tortuosity, connectivity, porosity, pore size) and fluid flow parameters (Re number distribution, permeability). Some channels exhibit Re values of 1400 at an inlet velocity of 1.6m/s, and a transition from Darcy's regime to a non-Darcian regime occurs around an inlet velocity of 0.02m/s. Location-dependent permeability ranges from 20-32 Darcy. Regression modelling reveals a strong correlation between fluid velocity and tortuosity at high inlet velocities, as well as with channel diameter at low inlet velocities. At higher inlet velocities, flow paths deviate more from the preferential direction, resulting in a decrease in the concentration parameter by an average of 0.4. This research provides valuable insights into the fluid flow behaviour within the meniscus and its structural influences.

The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues

Complex soft tissues, for example the knee meniscus, play a crucial role in mobility and joint health, but when damaged are incredibly difficult to repair and replace. This is due to their highly hierarchical and porous nature which in turn leads to their unique mechanical properties. In order to design tissue substitutes, the internal architecture of the native tissue needs to be understood and replicated. Here we explore a combined audio-visual approach - so called transperceptual - to generate artificial architectures mimicking the native ones. The proposed method uses both traditional imagery, and sound generated from each image as a method of rapidly comparing and contrasting the porosity and pore size within the samples. We have trained and tested a generative adversarial network (GAN) on the 2D image stacks. The impact of the training set of images on the similarity of the artificial to the original dataset was assessed by analyzing two samples. The first consisting of n=478 pairs of audio and image files for which the images were downsampled to 64 $\times$ 64 pixels, the second one consisting of n=7640 pairs of audio and image files for which the full resolution 256 $\times$ 256 pixels is retained but each image is divided into 16 squares to maintain the limit of 64 $\times$ 64 pixels required by the GAN. We reconstruct the 2D stacks of artificially generated datasets into 3D objects and run image analysis algorithms to characterize statistically the architectural parameters - pore size, tortuosity and pore connectivity - and compare them with the original dataset. Results show that the artificially generated dataset that undergoes downsampling performs better in terms of parameter matching. Our audiovisual approach has the potential to be extended to larger data sets to explore both how similarities and differences can be audibly recognized across multiple samples.