Enhancing trabecular CT scans based on deep learning with multi-strategy fusion.

Trabecular bone analysis plays a crucial role in understanding bone health and disease, with applications like osteoporosis diagnosis. This paper presents a comprehensive study on 3D trabecular computed tomography (CT) image restoration, addressing significant challenges in this domain. The research introduces a backbone model, Cascade-SwinUNETR, for single-view 3D CT image restoration. This model leverages deep layer aggregation with supervision and capabilities of Swin-Transformer to excel in feature extraction. Additionally, this study also brings DVSR3D, a dual-view restoration model, achieving good performance through deep feature fusion with attention mechanisms and Autoencoders. Furthermore, an Unsupervised Domain Adaptation (UDA) method is introduced, allowing models to adapt to input data distributions without additional labels, holding significant potential for real-world medical applications, and eliminating the need for invasive data collection procedures. The study also includes the curation of a new dual-view dataset for CT image restoration, addressing the scarcity of real human bone data in Micro-CT. Finally, the dual-view approach is validated through downstream medical bone microstructure measurements. Our contributions open several paths for trabecular bone analysis, promising improved clinical outcomes in bone health assessment and diagnosis.