Pareto Optimization of Tissue and Blood Vessel Growth in 3D Printed Bone Scaffolds

Abstract

Abstract Computational design is necessary for advancing biomedical technologies, particularly complex systems with numerous complicated trade-offs. For instance, 3D printed tissue scaffolds constructed as lattices necessitate consideration of tissue and vasculature growth trade-offs in relation to complex geometries. In this paper, curvature-based tissue growth models and agent-based vascularization simulations are used to predict growth. NSGA-II (non-dominated sorting genetic algorithm) is used for Pareto optimization of growth for heterogeneous unit cell scaffolds. Cube and BC-Cube (Body Centered-Cube) unit cells are considered with beam diameters from 64 to 313 μm that are arranged in lattices with No Voids or Channel Voids configurations. The Channel Voids configuration has channels consisting of no unit cells that promote unobstructed vascularization. Seeding the algorithm with high-performing scaffolds with homogenous unit cells improved search efficiency and quality, since unit cells can be configured either for high tissue growth or high vasculature growth. The Pareto front of solutions demonstrates that scaffolds with large porous areas for the Channel Voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the advantages in using NSGA-II for dual-objective search in complex biomedical systems, which provides a foundation for future multi-objective optimization for advanced tissue engineering systems.