Characterizing biomaterials like bone implants is crucial for understanding their interaction with biological tissues. Conventional histology and X-ray imaging methods, such as synchrotron radiation micro-computed tomography (SR$\mu$CT), are complementary techniques for this purpose. Histology provides high biochemical specificity but is destructive and limited to two-dimensional sections [1]. In contrast, SR$\mu$CT captures intact three-dimensional structures without physical sectioning or staining, offering detailed volumetric information, but its inherent greyscale contrast limits biochemical specificity. Combined, histology and SR$\mu$CT overcome their individual limitations in what is known as X-ray virtual histology, and with deep learning–based virtual staining this approach can now deliver stain-like specificity from label-free images in a fully non-destructive way [2].

Irvine et al. [3] showcased virtual staining for 3D X-ray imaging using a modified CycleGAN on paired SR$\mu$CT volumes, producing virtually stained datasets that enhance interpretability without additional sample preparation. This approach aligns with broader advances in modality transfer, where generative models translate between imaging techniques. For instance, Amano et al. [4] demonstrated that diffusion models effectively translate optical microscopy images into SEM, outperforming other approaches and highlighting their potential for cross-modality image synthesis.

Within the scope of this project, we aim to apply a diffusion model for modality transfer to generate a highly detailed virtual staining alternative. To achieve this, we assembled a collated and co-registered dataset of biodegradable magnesium implants in bone from prior studies [5,6], comprising over 50 SR$\mu$CT slices and corresponding stained histological sections (toluidine blue, $n > 30$ ; H&E, $n = 14$ ), enabling multi-scale assessment of degradation and osseointegration. The guided diffusion (GD) architecture was adapted from the base model, referred to as Palette [7]. We applied the colorization modality using two input images: the original histology as ground truth and the SR$\mu$CT slices as conditional images.

Preliminary results suggest that the GD model can generate synthetic histology images of bone–implant interfaces from SR $\mu$CT, indicating its potential as a feasible approach capable of capturing fine structural details. Although promising, further work is required to improve resolution, accuracy, and robustness. Future studies will investigate alternative diffusion-based architectures, such as the Super-Resolution via Repeated Refinement (SR3) model, to advance super-resolution imaging and provide a non-destructive strategy for comprehensive biomaterial characterization.

Reference

[1] T. Abraham, P. C. Costa, C. E. Filan, F. Robles, and R. M. Levenson, “Mode-mapping qOBM microscopy to virtual hematoxylin and eosin (H\&E) histology via deep learning,” SPIE-Intl Soc Optical Eng, May 2022, p. 58. doi: 10.1117/12.2622160.

[2] J. Albers et al., “X-ray-Based 3D Virtual Histology—Adding the Next Dimension to Histological Analysis,” Oct. 01, 2018, Springer New York LLC. doi: 10.1007/s11307-018-1246-3.

[3] S. C. Irvine, C. Lucas, D. Krüger, B. Guedert, J. Moosmann, and B. Zeller-Plumhoff, “Virtual staining for 3D X-ray histology of bone implants,” Sep. 2025, [Online]. Available: http://arxiv.org/abs/2509.09235

[4] N. Amano, B. Lei, M. Müller, F. Mücklich, and E. A. Holm, “Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI,” Mater Charact, vol. 220, Feb. 2025, doi: 10.1016/j.matchar.2024.114600.

[5] S. C. Irvine, C. Lucas, M. Bootbool, S. Galli, B. Zeller-Plumhoff, and J. P. Moosmann, “Multi-modal image registration and machine learning for the generation of 3D virtual histology of bone implants,” SPIE-Intl Soc Optical Eng, Aug. 2024, p. 70. doi: 10.1117/12.3028465.

[6] D. Krüger et al., “High-resolution ex vivo analysis of the degradation and osseointegration of Mg-xGd implant screws in 3D,” Bioact Mater, vol. 13, pp. 37–52, Jul. 2022, doi: 10.1016/j.bioactmat.2021.10.041.

[7] C. Saharia et al., “Palette: Image-to-Image Diffusion Models,” May 2022, [Online]. Available: http://arxiv.org/abs/2111.05826