From Pixel Generation to Topological Inference: Structural Dual Super-Resolution for Trustworthy Cross-Physical-Domain Trabecular Morphology Learning

2026-09-28

AI Summary

This study addresses the challenge of unreliable trabecular bone reconstruction arising from a 32-fold resolution gap and physical domain mismatch between clinical CT and synchrotron radiation micro-CT. To overcome this, we pioneer a topology inference paradigm that supersedes conventional pixel-wise generation, proposing a Structure-Dual Super-Resolution Network. Cross-domain morphological learning is achieved by coupling forward degradation with backward inference, further enhanced by a multi-scale structural consistency discriminator, quadruple structure-dual constraints, and a few-shot learning strategy. Experimental results demonstrate that six morphometric parameters align closely with high-resolution reference data, achieving an SSIM of 0.8. Furthermore, evaluations on independent source datasets validate both the cross-source generalization capability and the reliability of the proposed structural inference framework.

Abstract

Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SR{\mu}CT) provides 3.2{\mu}m high-resolution references but is not applicable for in vivo imaging. The two domains differ by 31.25x in resolution, are only coarsely paired, and have drastically different data volumes. Moreover, clinical UHRCT suffers from severe partial volume effects, strong noise, and beam hardening/scatter artifacts, while SR{\mu}CT is nearly free. Existing super-resolution networks and pretrained-prior methods underperform because they target pixel generation--diverse details and SSIM/PSNR--and do not explicitly model these physical differences. This indicates that 32x super-resolution via pixel generation is intrinsically ill-posed. We propose a paradigm shift from pixel generation to topological inference: deterministically predicting invariant microstructures from macro-scale low-resolution inputs, evaluated by morphological parameters. We realize this paradigm via structural dual super-resolution, coupling forward physical degradation (micro-to-macro) with inverse structural inference (macro-to-micro) through structural duality constraints. The method is an end-to-end, few-shot, compact structural dual network (SDN), comprising a bidirectional modeling network for forward degradation and inverse reconstruction, a pyramid structural consistency discriminator, and four structural duality constraints. On the testset, SDN achieves morphological parameters largely consistent with SR{\mu}CT across 7 metrics, enabling clinical UHRCT with micro-imaging-level morphological quantification, with SSIM reaching 0.8. Trained on 3.2{\mu}m SSRF data, the model generalizes well to 3.25{\mu}m BSRF data from an independent source, validating cross-source generalization and confirming that the designed network achieves trustworthy structural inference rather than pixel generation.

Problem

• Super-Resolution
• Trabecular Morphology
• Cross-Physical-Domain
• Topological Inference
• Pixel Generation

Innovation

• Topological Inference
• Structural Dual Super-Resolution
• Trabecular Morphology
• Cross-Physical-Domain
• Structural Duality Constraints