ArticleJournal of medical imaging (Bellingham, Wash.)2026
Conditional generative diffusion model for 3D trabecular bone synthesis with tunable microstructure.
Article in Journal of medical imaging (Bellingham, Wash.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: We aim to develop a conditional generative diffusion model capable of producing three-dimensional (3D) trabecular bone samples that can be tuned to achieve specific structural characteristics prescribed in terms of three geometric metrics of trabecular microarchitecture: bone volume fraction (BV/TV), trabecular thickness (Tb.Th), and spacing (Tb.Sp). Approach: The generative model is based on 3D latent diffusion. The latent representation of trabecular patches is obtained by a dedicated variational autoencoder (VAE). To control the microstructure characteristics of the synthetic samples, the model is conditioned on BV/TV, Tb.Th, and Tb.Sp. In addition, a shifting slab inference method is employed to generate extended volumes with locally tunable microstructure in a computationally efficient manner. The training data involved 3551 Results: Visually, the synthesized cancellous bone patches appear highly similar to the training micro-CT data. The conditioned parameters of the generated volumes agree well with their target values (PCC of 0.99, 0.97, and 0.95 for BV/TV, Tb.Th, and Tb.Sp, respectively). There is a trend toward generating trabeculae that are slightly thicker than prescribed, but this bias is typically on the order of one voxel ( Conclusion: The proposed generative model is capable of generating realistic digital trabecular bone patches. The application of latent space diffusion using a dedicated VAE augmented with the shifting-slab mechanism effectively overcomes computer memory constraints to enable the synthesis of 3D volumes. The conditioning mechanism is effective in guiding the synthesis toward desired microstructural characteristics. Possible applications include virtual clinical trials of new skeletal image biomarkers and establishing priors for advanced image reconstruction.
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