Guha Indranil, Nadeem Syed Ahmed, You Chenyu, Zhang Xiaoliu, Levy Steven M, Wang Ge, Torner James C, Saha Punam K
Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, IA 52242.
Department of Computer Science, Yale University, New Haven, CT 05620.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11317. doi: 10.1117/12.2549318. Epub 2020 Feb 28.
Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and high-resolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric for low-resolution data. Different Tb microstructural measures such as thickness, spacing, and network area density are also computed from low- and predicted high-resolution images, and compared with the values derived from true high-resolution scans. Thickness and network area measures from predicted images showed higher agreement with true high-resolution CT (CCC = [0.95, 0.91]) derived values than the same measures from low-resolution images (CCC = [0.72, 0.88]).
骨质疏松症是一种常见的与年龄相关的疾病，其特征是骨密度降低和骨折风险增加。小梁骨（Tb）的微观结构质量常见于轴向骨骼部位和长骨末端，是骨强度和骨折风险的重要决定因素。高分辨率的新型CT扫描仪能够测量外周部位的Tb微观结构。然而，微观结构测量对分辨率的依赖性、各种CT扫描仪之间广泛的分辨率差异以及技术的快速升级，使得基于CT的横断面和纵向骨研究中的数据协调成为必要。本文提出了一种基于深度学习的方法，使用GAN-CIRCLE从低分辨率CT扫描中对Tb微观结构进行高分辨率重建。利用19名志愿者在低分辨率和高分辨率CT扫描仪上进行的踝关节CT扫描后配准的数据，开发并评估了一个网络。从10名志愿者中随机采集9000对大小为64×64的低分辨率和高分辨率匹配图像块用于训练和验证。另外从其他9名志愿者中采集5000对匹配图像块用于评估。定量比较表明，与低分辨率数据的相同指标相比，预测的高分辨率扫描与真实高分辨率扫描的结构相似性指数显著提高（p < 0.01）。还从低分辨率和预测的高分辨率图像中计算了不同的Tb微观结构测量值，如厚度、间距和网络面积密度，并与从真实高分辨率扫描中得出的值进行比较。预测图像的厚度和网络面积测量值与真实高分辨率CT（CCC = [0.95, 0.91]）得出的值的一致性高于低分辨率图像的相同测量值（CCC = [0.72, 0.88]）。
