Abstract：
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.
摘要：
骨小梁分析在了解骨骼健康和疾病中起着至关重要的作用。应用像骨质疏松症诊断。本文对三维骨小梁CT图像复原进行了全面的研究,解决这一领域的重大挑战。这项研究引入了一个骨干模型，级联-SwinUNETR,单视图三维CT图像复原。该模型利用具有监督和Swin-Transformer功能的深层聚合，在特征提取方面表现出色。此外,这项研究还带来了DVSR3D，双视图恢复模型，通过深度特征融合与注意力机制和自动编码器实现良好的性能。此外，介绍了一种无监督域自适应(UDA)方法，允许模型在没有额外标签的情况下适应输入数据分布，在现实世界的医疗应用中拥有巨大的潜力，并消除了对侵入性数据收集程序的需求。该研究还包括用于CT图像复原的新的双视图数据集的策展，解决Micro-CT中真实人体骨骼数据的稀缺性。最后,通过下游医学骨微结构测量验证了双视图方法。我们的贡献为骨小梁分析开辟了几条途径，有望改善骨健康评估和诊断的临床结果。
