arXiv subjects

Sara Cardona

Publications and source records attributed to Sara Cardona.

3 recordsLinked to original sources

arXiv2026

ToFiE, a Topology-aware Fiber Extraction workflow for 3D reconstruction of dense and heterogeneous biological fiber networks from microscopy images

Fibrous networks are ubiquitous structural components in biology, spanning cellulose in plant cell walls, fibrin in blood clots, and collagen in the extracellular matrix of animal tissues. Theoretical models predict that network connectivity critically influences their mechanical behavior. However, accurately reconstructing network topology from 3D image data remains a major challenge as current segmentation methods are not designed to preserve network topology and often rely on intensity-based thresholding, which can fragment fibers and distort junction connectivity. Here, we introduce ToFiE, an open-source topology-aware fiber extraction workflow for reconstructing dense and heterogeneous fibrous networks from high resolution microscopy images while preserving connectivity in three dimensions. We validate ToFiE using synthetic fluorescence microscopy images of fiber networks with varying topologies and signal-to-noise ratios. We further demonstrate its performance by reconstructing the fiber networks of a library of collagen gels with various microstructures, imaged using confocal fluorescence microscopy. Altogether, the results establish ToFiE as a practical semi-automated framework for extracting mechanically relevant network information from imaging data across a broad range of fibrous materials.

q-bio.QM↗

arXiv2025

A Multiscale Framework for In Silico Thrombus Generation and Photoacoustic Simulations

Thrombus microstructure plays a critical role in determining the treatment success for thrombus-related diseases such as stroke and deep vein thrombosis. However, no in vivo diagnostic method can fully capture thrombus microstructure yet. Photoacoustic imaging is uniquely positioned to provide information on thrombi composition as it relays optical absorption information from diffuse photons at acoustic propagation depths. Computational modeling enables systematic exploration of microstructural effects on imaging signals, offering insights into developing improved in vivo diagnostic techniques. However, no photoacoustic simulation platform can model microstructural features within centimeter-scale phantoms at reasonable computational cost. Here, we present REFINE, a topology-driven framework for generating in silico thrombi replicating its key replicating their key microstructural traits. REFINE enables controlled, recursive optimization of thrombus topology, making it suitable for accurate photoacoustic modeling and potentially powerful for biomechanical analyses. These digital thrombi are embedded into a multiscale photoacoustic simulation platform that bridges microscale acoustic modeling with macroscale thrombus geometries, enabling efficient and realistic simulation of photoacoustic signal responses. We created unique thrombi microstructure for various compositions and porosities. Our simulation framework effectively links microstructural features to macroscale imaging outcomes, in agreement with previous empirical studies. Our results demonstrate that thrombus microstructure significantly affects photoacoustic spectral responses and can be modeled in silico. These findings highlight that multiscale photoacoustic simulation is a powerful tool for characterizing tissue microstructure and that our framework enables in silico thrombi analysis and diagnostic imaging strategy development

physics.med-ph↗

arXiv2025

TopoGEN: topology-driven microstructure generation for in silico modeling of fiber network mechanics

The fields of mechanobiology and biomechanics are expanding our understanding of the complex behavior of soft biological tissues across multiple scales. Given the intricate connection between tissue microstructure and its macroscale mechanical behavior, unraveling this mechanistic relationship remains an ongoing challenge. Reconstituted fiber networks serve as valuable in vitro models to simplify the intricacy of in vivo systems for targeted investigations. Concurrently, advances in imaging enable microstructure visualization and, through generative pipelines, modeling as discrete element networks. These mesoscale models provide insights into macroscale tissue behavior. However, there is still no clear way to systematically incorporate experimentally observed microstructural changes into in silico models of biological networks. In this work, we develop a novel framework to generate topologically-driven discrete fiber networks using high-resolution images that account for how environmental changes during polymerization influence the resulting structure. Leveraging these networks, we generate models of interconnected load-bearing fiber components that exhibit softening under compression and are bending-resistant. The generative topology framework enables control over network-level features, such as fiber volume fraction and cross-link density, along with fiber-level properties, like length distribution, to simulate changes driven by different polymerization conditions. We validate the robustness of our simulations against experimental data in a collagen-specific study case where we examine nonlinear elastic responses of collagen networks across varying conditions. TopoGEN provides a tool for tissue biomechanics and engineering, helping to bridge microstructural insights and bulk mechanical behavior by linking image-derived microstructural topological organization to soft tissue mechanics.

cond-mat.soft↗