FoamQuant: a Python package for time-resolved 3D image quantification of cellular materials

X-ray tomography is a well established technique for investigating three-dimensional bulk structures across scales, from macroscopic samples down to their microscopic constituents. The addition of a temporal dimension through dynamic, time-resolved acquisition results in four-dimensional datasets whose complexity often exceeds the processing capabilities of existing image analysis tools. To address the urgent need for a dedicated four-dimensional image analysis platform for cellular materials, we present FoamQuant—a free and open-source software package designed for batch processing and quantitative analysis of large time series of evolving cellular or foam-like materials. FoamQuant enables the extraction of key parameters such as liquid fraction (porosity), individual bubble (pore) volume and offers advanced characterization of mechanical properties, including elastic strain and stress fields as well as individual cell rearrangements. Its user-friendly, modular architecture is demonstrated through two case studies: (i) the orientation of plastic events in a flowing liquid foam, and (ii) bubble tracking in a coarsening albumin foam.