TriTopo-LGDM: A reverse design method for trabecular bone scaffolds integrating topology optimization and latent graph diffusion models

The efficient design of cancellous bone tissue engineering scaffolds that closely replicate both the histological and mechanical characteristics of native cancellous bone remains a significant challenge in the field. The vast design space of porous scaffolds gives rise to a highly complex, nonlinear relationship between geometric morphology, mechanical properties, and osteogenic performance. Currently, the design and in vitro fabrication of scaffolds for cancellous bone defect repair largely depend on expert-driven forward design methods. These approaches are time-consuming, costly, and often lack reproducibility and controllability, limiting their suitability for clinical applications. To overcome these limitations, this study introduces an innovative inverse design framework – TriTopo-LGDM – which combines topological optimization priors with a latent graph diffusion generative model. Built on a triply aligned dataset encompassing structure, physics, and optimization, the framework establishes a scaffold generation pipeline specifically tailored for cancellous bone defect reconstruction. It enables the efficient generation and accurate modeling of multi-scale functional scaffold structures. Experimental evaluations confirm that TriTopo-LGDM establishes a robust bidirectional mapping between topological parameters and target mechanical properties, significantly reducing design time and cost while improving structural consistency and 3D printability. Mechanical testing and finite element simulations further validate the strong mechanical and morphological resemblance of the generated scaffolds to natural cancellous bone. This work presents a generalizable and efficient strategy for the rapid, patient-specific design of implants that promote cancellous bone regeneration.