Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.
发表日期： 2017.10.18 来源：J Digit Imaging. 2017 Oct 18.
Umehara K1, Ota J1, Ishida T1.
1. Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita, 565-0871, Japan.
在这项研究中，超分辨率卷积神经网络（SRCNN）是一种新兴的基于深度学习的超分辨率方法，用于提高胸部CT图像的分辨率，并采用后处理方法进行评估。为了进行评估，从癌症成像档案库中采集了89个胸部CT病例。 89例CT随机分为45例实验训练组，44例外部测试组。 SRCNN是通过训练数据集进行训练。使用训练的SRCNN，从原始测试图像下采样的低分辨率图像重建高分辨率图像。为了定量评估，测量两个图像质量度量并将其与传统的线性插值方法进行比较。 SRCNN方案的图像恢复质量显著高于线性插值方法（p<0.001或p<0.05）。通过SRCNN方案重建的高分辨率图像高度恢复并且与原始参考图像相差无几，特别是对于a×2放大率。这些结果表明，在增强胸部CT图像的图像分辨率上，SRCNN方案显著优于线性插值方法。结果还表明SRCNN可能成为从标准CT图像生成高分辨率CT图像的一种潜在解决方案。
In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.
Artificial intelligence; Computed tomography; Deep learning; High-resolution medical imaging; Super resolution; Super-resolution convolutional neural network
阅读原文：PMID: 29047035 DOI: 10.1007/s10278-017-0033-z