Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT
Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT
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DOI：
10.1007/s10278-017-0033-z
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发表时间：
2018-08-01
影响因子：
4.4
通讯作者：
Ishida, Takayuki
中科院分区：
文献类型：
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作者：
Umehara, Kensuke;Ota, Junko;Ishida, Takayuki
在这项研究中，超分辨率卷积神经网络（SRCNN）方案是一种新兴的基于深度学习的超分辨率方法，用于增强胸部CT图像的图像分辨率，并使用后处理方法进行了评估。为了进行评估，从癌症成像档案中抽取了89例胸部CT病例。将89例CT病例随机分为45例训练病例和44例外测病例。使用训练数据集训练SRCNN。使用训练好的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。对于定量评价，测量两个图像质量指标，并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被高度恢复，并且与原始参考图像相当，特别是对于x2放大率。这些结果表明，SRCNN方案显着优于线性插值方法，用于提高胸部CT图像的图像分辨率。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 x2 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.