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

Area of Science:

• Materials Science
• Physics
• Biotechnology

Background:

• X-ray tomography is crucial for 3D structural analysis.
• Dynamic 4D datasets from time-resolved X-ray tomography present significant image analysis challenges.
• Existing tools struggle with the complexity of 4D cellular material data.

Purpose of the Study:

• To introduce FoamQuant, a novel, free, and open-source software package.
• To provide a dedicated platform for batch processing and quantitative analysis of 4D cellular or foam-like materials.
• To enable detailed characterization of evolving material structures over time.

Main Methods:

• Development of FoamQuant, a modular and user-friendly software package.
• Implementation of algorithms for batch processing of large 4D time-series datasets.
• Application of FoamQuant to analyze liquid foam dynamics and bubble coarsening.

Main Results:

• FoamQuant successfully extracts key parameters like liquid fraction (porosity) and individual bubble volume.
• Advanced characterization of mechanical properties, including elastic strain and stress fields, is achieved.
• Individual cell rearrangements and plastic events in flowing foams are quantified.

Conclusions:

• FoamQuant addresses the urgent need for specialized 4D image analysis tools for cellular materials.
• The software facilitates in-depth understanding of material evolution, mechanical properties, and dynamic processes.
• Demonstrated utility through case studies on liquid foams and coarsening albumin foams.