Void space and scaffold analysis of packed particles: applications in granular biomaterials

Abstract We have developed a tool for analyzing packed particles in response to the growing popularity of granular biomaterials. Granular hydrogels, including microporous annealed particle (MAP) scaffolds, are a class of material used for therapeutic applications due to their unique properties, including micro-porosity between particles. The microarchitecture of granular materials is challenging to study, which leads many in the field to report the unreliable metric of void volume fraction and/or 2D-slice approximations to ‘pore size’ as the only characterizations of void space. In response, we have created LOVAMAP, a custom software that combines techniques from computational geometry and graph theory to segment the void space into 3-D pores, which are the natural pockets of open space. LOVAMAP’s 44 scaffold features provide the user with a quantitative profile that describes both the interior and entrances of the scaffold. Our visually-rich outputs address topics like void space size, shape, connectivity, paths, isotropy/anisotropy, ligand availability, and infiltration/migration restrictions. Using LOVAMAP, we have studied 60 different types of granular scaffolds, including real MAP scaffolds with corresponding cell data. We use higher dimensional analysis to show that output data from our software can be used to classify types of 3-D pores, as well as characterize entire scaffolds by generating a numeric ‘fingerprint.’ In conjunction with cell data, LOVAMAP reveals relationships between neurosphere formation and scaffold void space geometry. LOVAMAP is an enabling technology with broad applications for granular biomaterial research and for all fields that study granular material.