A Super-Resolution Diffusion Model for Recovering Bone Microstructure from CT Images.
A Super-Resolution Diffusion Model for Recovering Bone Microstructure from CT Images.
复制标题
用于从 CT 图像中恢复骨微结构的超分辨率扩散模型。
DOI：
10.1148/ryai.220251
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Rajapakse,ChamithS
中科院分区：
文献类型：
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作者：
Chan,TrevorJ;Rajapakse,ChamithS
目的利用基于弥散的深度学习模型从股骨近端低分辨率图像中恢复骨骼微观结构，股骨近端是创伤性骨质疏松性骨折的常见部位。材料和方法本回顾性研究的测试和测试数据包括高分辨率尸体微ct扫描（n= 26），作为基本事实。这些图像在用于模型训练之前被下采样。该模型用于将这些低分辨率图像的空间分辨率提高三倍，从0.72 mm提高到0.24 mm，足以显示骨骼微观结构。利用微观结构指标和有限元模拟得出的小梁区域刚度验证了模型的性能。性能也通过少数图像质量评估指标进行评估。使用类内相关系数（ICCs）和Pearson相关系数评估模型性能与真实值之间的相关性。与流行的深度学习基线相比，所提出的模型在生理指标上表现出更高的准确性（所提出模型的平均ICC为0.92，而次优方法的ICC为0.83）和更低的偏差（平均差值为3.80%，分别为10.00%）。两个基于梯度的图像质量指标与结构和机械标准的精度密切相关（r> 0.89）。结论该方法可以在与当前临床成像方案相当的辐射剂量下精确测量骨结构和强度，提高临床CT评估骨健康的可行性。关键词：CT，图像后处理，骨-阑尾，长骨，辐射效应，量化，预后，半监督学习©RSNA， 2023
PurposeTo use a diffusion-based deep learning model to recover bone microstructure from low-resolution images of the proximal femur, a common site of traumatic osteoporotic fractures.Materials and MethodsTraining and testing data in this retrospective study consisted of high-resolution cadaveric micro-CT scans (n= 26), which served as ground truth. The images were downsampled prior to use for model training. The model was used to increase spatial resolution in these low-resolution images threefold, from 0.72 mm to 0.24 mm, sufficient to visualize bone microstructure. Model performance was validated using microstructural metrics and finite element simulation–derived stiffness of trabecular regions. Performance was also evaluated across a handful of image quality assessment metrics. Correlations between model performance and ground truth were assessed using intraclass correlation coefficients (ICCs) and Pearson correlation coefficients.ResultsCompared with popular deep learning baselines, the proposed model exhibited greater accuracy (mean ICC of proposed model, 0.92 vs ICC of next best method, 0.83) and lower bias (mean difference in means, 3.80% vs 10.00%, respectively) across the physiologic metrics. Two gradient-based image quality metrics strongly correlated with accuracy across structural and mechanical criteria (r> 0.89).ConclusionThe proposed method may enable accurate measurements of bone structure and strength with a radiation dose on par with current clinical imaging protocols, improving the viability of clinical CT for assessing bone health.Keywords:CT, Image Postprocessing, Skeletal-Appendicular, Long Bones, Radiation Effects, Quantification, Prognosis, Semisupervised Learning© RSNA, 2023