A Conditional Generative Diffusion Model of Trabecular Bone with Tunable Microstructure

We developed a generative model capable of producing synthetic trabecular bone that can be precisely tuned to achieve specific structural characteristics, such as bone volume fraction (BV/TV), trabecular thickness (Tb.Th), and spacing (Tb.Sp).

The generative model is based on Diffusion Transformers (DiT), a latent diffusion approach employing a transformer architecture in the denoising network. To control the microstructure characteristics of the synthetic trabecular bone samples, the model is conditioned on BV/TV, Tb.Th, and Tb.Sp. The training data involved 29898 256×256-pixel Regions of Interest (ROIs) extracted from micro-CT volumes ( $50\mu \text{m}$ voxel size) of 20 femoral bone specimens, paired with trabecular metrics computed within each ROI; the training/validation split was 9:1. For testing, 3499 synthetic bone samples were generated over a wide range of condition (target) microstructure metrics. Results were evaluated in terms of (i) the ability to cover real-world distribution of trabecular structures (coverage), (ii) agreement with target metric values (Pearson Correlation), and (iii) consistency of the metrics across multiple realizations of the DiT model with fixed condition (Coefficient of Variation, CV).

The model achieved good coverage of real-world bone microstructures and visual similarity to true trabecular ROIs. Pearson Correlations against the condition (target) metric values were high: 0.9540 for BV/TV, 0.9618 for Tb.Th, and 0.9835 Tb.Sp. Microstructural characteristics of the synthetic samples were stable across DiT realizations, with CV ranging from 3.37% to 11.78% for BV/TV, 2.27% to 3.22% for Tb.Th, and 2.53% to 5.00% for Tb.Sp.

The proposed generative model is capable of generating realistic digital trabecular bones that can be precisely tuned to achieve specified microstructural characteristics. Possible applications include virtual clinical trials of new skeletal image biomarkers and establishing priors for advanced image reconstruction.