CT image super-resolution reconstruction based on multi-scale residual network

Lei WU; Guo-qiang LYU; Chen ZHAO; Jie-chao SHENG; Qi-bin FENG

doi:10.3788/YJYXS20193410.1006

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CT image super-resolution reconstruction based on multi-scale residual network

Image Processing | 更新时间：2020-08-12

- CT image super-resolution reconstruction based on multi-scale residual network
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 10, Pages: 1006-1012(2019)
- 作者机构：
  
  1.合肥工业大学特种显示技术国家工程实验室现代显示技术省部共建国家重点实验室光电技术研究院, 安徽合肥 230009
  2.合肥工业大学电子科学与应用物理学院, 安徽合肥 230009
  3.合肥工业大学仪器科学与光电工程学院, 安徽合肥 230009
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193410.1006
  CLC： TP391.4
- Received：13 June 2019，
  
  Accepted：18 August 2019，
  
  Published：05 October 2019
- 稿件说明：
移动端阅览
Lei WU, Guo-qiang LYU, Chen ZHAO, et al. CT image super-resolution reconstruction based on multi-scale residual network[J]. Chinese journal of liquid crystals and displays, 2019, 34(10): 1006-1012.
DOI：

Lei WU, Guo-qiang LYU, Chen ZHAO, et al. CT image super-resolution reconstruction based on multi-scale residual network[J]. Chinese journal of liquid crystals and displays, 2019, 34(10): 1006-1012. DOI： 10.3788/YJYXS20193410.1006.

摘要

为了将超分辨率重建算法应用于医学影像领域，提升各类医学影像的分辨率，针对当前主流算法网络结构和分辨率提升倍数的尺度单一性问题，提出了一种应用于CT图像的多尺度残差网络模型。首先，通过级联多层残差块构建模型框架，残差块内采用3种尺度的卷积核提取低分辨率图像的细节特征。然后，将特征图融合在一个维度进行特征映射和数据降维，并将多尺度特征信息导入下一残差块。最后，将网络学习到的残差图与低分辨率图像融合，重建高分辨率图像。采用经过多种放大倍数处理的CT图像对网络进行混合训练，实现了一个模型可以同时支持多种倍数的分辨率提升。实验结果表明：在2，3，4倍放大因子下，该模型重建的CT图像PSNR平均较VDSR算法高0.87，0.83，1.16 dB。因此，本文模型有效提升了CT图像的超分辨率重建效果，更锐利地恢复了其细节特征，同时大大提升了算法实用性。

Abstract

In order to apply the super-resolution reconstruction algorithm to the field of medical imaging and improve the resolution of various medical images

a multi-scale residual network model applied to CT images is proposed for the problem of the singleness of the network structure and the resolution multiplier of the current mainstream algorithm. Firstly

the model framework is built by cascading multi-level residual blocks

and the convolution kernels of three scales are used in the residual block to extract the detailed features of low-resolution images. Then

the feature map is fused in one dimension for feature mapping and data dimensionality reduction

and the multi-scale feature information is imported into the next residual block. Finally

the residual map calculated by the network is merged with the low-resolution image to reconstruct the high-resolution image. The network is trained by CT images processed by multiple magnifications

and so that a model can support multiple resolution enhancements at the same time. The experimental results show that under the 2

and 4 times magnification factors

the PSNR of the reconstructed CT image is 0.87

0.83

1.16 dB higher than the VDSR algorithm. Therefore

the model of this paper effectively improves the super-resolution reconstruction effect of CT images

restores its detailed features more sharply

and greatly improves the practicality of the algorithm.

关键词

Keywords

references

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