딥러닝을 이용한 보간 및 두께 축소를 통한 요골 원위부 골절의 컴퓨터 단층 촬영 영상 향상에 대한 임상 평가

Abstract

Backgrounds: 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. Purpose: This study aims to utilize conditional Generative Adversarial Networks (cGAN) to improve the resolution of 3mm CT images (CT enhancement). Methods: Following institutional review board approval, 3mm-1mm paired CT data from 11 patients with DRFs were collected. cGAN was used to improve the resolution of 3mm CT images to match that of 1mm images (CT enhancement). Two distinct methods were employed for training and generating CT images. In Method 1, a 3mm CT raw image was used as input with the aim of generating a 1mm CT raw image. Method 2 was designed to emphasize the difference value between the 3mm and 1mm images; using a 3mm CT raw image as input, it produced the difference in image values between the 3mm and 1mm 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). Results: 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 1mm slice image (15 vs 7, p=201). Conclusion: In our study utilizing cGAN for enhancing CT imaging resolution, the authors found that the method, which focuses on the difference value between 3mm and 1mm images (Method 2), consistently outperformed traditional techniques. Keywords: distal radius fracture, high-resolution computed tomography, conditional generative adversarial networks, enhancement