Waveletdomain dilated network for fast low-dose CT image reconstruction

被引：0

作者：

Li K. ^{[1
,2
]}

Zhang L. ^{[1
]}

Xu H. ^{[2
]}

Song H. ^{[1
,3
]}

机构：

[1] Educational Technology and Network Center, Chang'an University, Xi'an

[2] School of Electronic Control, Chang'an University, Xi'an

[3] School of Information Engineering, Chang'an University, Xi'an

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2020年 / 47卷 / 04期

关键词：

Computed tomography; Convolutional neural networks; Image reconstruction; Wavelet transforms;

D O I：

10.19665/j.issn1001-2400.2020.04.012

中图分类号：

学科分类号：

摘要：

Low-dose CT has the advantages of low radiation and high efficiency, but the noise and artifacts with low-dose CT images reduce the reliability of diagnosis. In order to improve the quality of low-dose CT images, this paper attempts to enhance the visuals of reconstructed CT images in the wavelet domain, and improve the running speed by combining the multi-dilated convolution and subpixel, so that the model can be better deployed to the CT equipment. The data set of "2016 AAPM Low Dose CT image Challenge" is used to evaluate the proposed method. Experimental results show that the visuals of reconstructed CT images are better. Compared with RED-CNN, the average PSNR of the proposed method is improved by 0.1428dB (1mm) / 0.0939dB (3mm), and the running speed on the CPU and GPU is increased by more than 55% and 50%, respectively. © 2020, The Editorial Board of Journal of Xidian University. All right reserved.

引用

页码：86 / 93

页数：7

共 16 条

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