VAE-based synthetic volume generation for trabecular bone microarchitecture.

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Abstract

Micro computed tomography (microCT) enables detailed characterization of trabecular bone microarchitecture through quantitative morphological parameters. However, trabecular bone exhibits morphological variability across species, anatomical locations, bone types, and physiological or pathological conditions. Individual microCT datasets generally represent only a limited portion of this morphological variability, which may constrain the development and validation of quantitative descriptors, image-processing pipelines, and computational models. This study presents a Variational Autoencoder (VAE)-based framework for generating synthetic trabecular bone images, with the potential to support more comprehensive investigations of trabecular microarchitecture. The VAE is trained on individual microCT slices and learns a three-dimensional latent space in which the ordered sequence of slices is mapped onto a continuous trajectory. Generating a synthetic volume reduces to i) defining a latent space trajectory and ii) decoding its points into 2D images ensuring inter-slice coherence without requiring any explicit 3D architecture. An interactive trajectory editor allows the user to manipulate, extend, or resample the latent path, providing full control over the morphology, length, and resolution of the synthetic volume. The reconstruction capability of the VAE was assessed by comparing the original microCT slices with their VAE-based reconstructions, and the original microCT-derived volume with the corresponding 3D volume obtained from the VAE-based reconstructed images, using 2D and 3D descriptors of trabecular microarchitecture. New sequences of synthetic images could be generated by modifying latent-space trajectories through the editor, thereby enabling the generation of multiple synthetic 3D volumes with controlled morphological variability. In this study, one such sequence was generated, and the corresponding 3D synthetic volume was characterized using microarchitectural parameters. The resulting values were then compared with those reported in the literature to assess the plausibility of the generated structure. Comparison of the morphological descriptors showed a high degree of similarity between the VAE-reconstructed and original microCT images. The corresponding 3D volumes also exhibited comparable morphological characteristics, indicating that the reconstruction process preserved the main structural features of the original microCT sample. Finally, the synthetic volume generated by synthetic VAE-based images exhibited microarchitectural parameter values comparable to those reported in the literature. These results provide support for the ability of the proposed framework to generate synthetic trabecular microarchitectures with coherent structural features. Therefore, the framework can support data augmentation for deep-learning model training and more extensive investigations of trabecular microarchitecture. Future developments will focus on linking selected morphological descriptors to the three latent-space axes, thereby expanding the range of microarchitectural variability that can be generated and investigated.

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