Diffusion Model-Based Design of Bionic Bone Scaffolds with Tunable Microstructures.

purposeIn the clinical treatment of bone defects that exceed the critical size threshold, traditional methods using metal fixation devices, autografts, and allografts exhibit significant limitations. Meanwhile, bone scaffolds with minimal risks of secondary injury, low immune rejection are emerging as a promising alternative. The effective design of porosity, pore size, and trabecular thickness in bone scaffolds is critical; however, current strategies often struggle to optimally balance these parameters. Here, we propose a bionic bone scaffold design method that mimics multiple properties of natural cancellous bone using a diffusion model.

methodsFirst, we develop a classifier-free conditional diffusion model and train it on a Micro-CT (μCT) image dataset of porcine vertebral cancellous bone. The training model can produce personalized 2-dimensional images of natural-like bone with tunable microstructures. Subsequently, we stack images layer by layer to form 3-dimensional scaffolds, mimicking the CT/μCT image reconstruction process. Finally, computational fluid dynamics analysis is conducted to validate the scaffold models' fluid properties, while bioresin bone scaffold samples are 3D-printed for mechanical testing and biocompatibility assessment.

resultsThe three key morphological parameters of the generated images-porosity (50-70%), pore size (468-936 μm), and trabecular thickness (156-312 μm)-can be precisely and independently controlled. Fluid simulation and mechanical testing confirm scaffolds' robust performance in permeability (10⁻⁹ to 10⁻⁸ m

conclusionThese promising results highlight the potential of our method for the personalized design of scaffolds to effectively repair large bone defects.