Department of Orthopedic Surgery, Hospital of Chung-Ang University of Medicine, Dongjak-gu, Seoul, Republic of Korea.
Department of Orthopedic Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
PLoS One. 2024 Aug 16;19(8):e0308346. doi: 10.1371/journal.pone.0308346. eCollection 2024.
BACKGROUND/PURPOSE: Distal radius fractures (DRFs) account for approximately 18% of fractures in patients 65 years and older. While plain radiographs are standard, the value of high-resolution computed tomography (CT) for detailed imaging crucial for diagnosis, prognosis, and intervention planning, and increasingly recognized. High-definition 3D reconstructions from CT scans are vital for applications like 3D printing in orthopedics and for the utility of mobile C-arm CT in orthopedic diagnostics. However, concerns over radiation exposure and suboptimal image resolution from some devices necessitate the exploration of advanced computational techniques for refining CT imaging without compromising safety. Therefore, this study aims to utilize conditional Generative Adversarial Networks (cGAN) to improve the resolution of 3 mm CT images (CT enhancement).
Following institutional review board approval, 3 mm-1 mm paired CT data from 11 patients with DRFs were collected. cGAN was used to improve the resolution of 3 mm CT images to match that of 1 mm images (CT enhancement). Two distinct methods were employed for training and generating CT images. In Method 1, a 3 mm CT raw image was used as input with the aim of generating a 1 mm CT raw image. Method 2 was designed to emphasize the difference value between the 3 mm and 1 mm images; using a 3 mm CT raw image as input, it produced the difference in image values between the 3 mm and 1 mm CT scans. Both quantitative metrics, such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM), and qualitative assessments by two orthopedic surgeons were used to evaluate image quality by assessing the grade (1~4, which low number means high quality of resolution).
Quantitative evaluations showed that our proposed techniques, particularly emphasizing the difference value in Method 2, consistently outperformed traditional approaches in achieving higher image resolution. In qualitative evaluation by two clinicians, images from method 2 showed better quality of images (grade: method 1, 2.7; method 2, 2.2). And more choice was found in method 2 for similar image with 1 mm slice image (15 vs 7, p = 201).
In our study utilizing cGAN for enhancing CT imaging resolution, the authors found that the method, which focuses on the difference value between 3 mm and 1 mm images (Method 2), consistently outperformed.
背景/目的：桡骨远端骨折（DRF）约占 65 岁及以上患者骨折的 18%。虽然标准的影像学检查是普通 X 线，但高分辨率计算机断层扫描（CT）对于诊断、预后和干预计划至关重要的详细成像的价值越来越被认可。CT 扫描的高清 3D 重建对于骨科 3D 打印等应用以及骨科诊断中移动 C 臂 CT 的实用性至关重要。然而，一些设备的辐射暴露和图像分辨率不理想的问题需要探索先进的计算技术来改进 CT 成像，同时又不影响安全性。因此，本研究旨在利用条件生成对抗网络（cGAN）来提高 3 毫米 CT 图像（CT 增强）的分辨率。
在获得机构审查委员会批准后，收集了 11 名 DRF 患者的 3 毫米-1 毫米配对 CT 数据。cGAN 用于提高 3 毫米 CT 图像的分辨率，使其与 1 毫米图像匹配（CT 增强）。采用两种不同的方法进行训练和生成 CT 图像。在方法 1 中，将 3 毫米 CT 原始图像作为输入，旨在生成 1 毫米 CT 原始图像。方法 2 旨在强调 3 毫米和 1 毫米图像之间的差值；使用 3 毫米 CT 原始图像作为输入，生成 3 毫米和 1 毫米 CT 扫描之间的图像值差值。两位骨科医生进行了定量评估，使用峰值信噪比（PSNR）、均方误差（MSE）和结构相似性指数（SSIM）等定量指标，以及通过定性评估来评估图像质量，评估等级（1~4，数字越低表示分辨率质量越高）。
定量评估表明，我们提出的技术，特别是在方法 2 中强调差值的技术，在实现更高的图像分辨率方面始终优于传统方法。在两位临床医生的定性评估中，方法 2 的图像质量更好（等级：方法 1，2.7；方法 2，2.2）。并且在方法 2 中可以找到更多与 1 毫米切片图像相似的图像选择（15 对 7，p=201）。
在我们利用 cGAN 增强 CT 成像分辨率的研究中，作者发现，重点关注 3 毫米和 1 毫米图像之间差值的方法（方法 2）始终表现更好。
