小波域扩张网络用于低剂量CT图像快速重建

doi:10.19665/j.issn1001-2400.2020.04.012

Abstract

Abstract:

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.

Key words: image reconstruction, computed tomography, wavelet transforms, convolutional neural networks

CLC Number:

TP391

LI Kunlun,ZHANG Lu,XU Hongke,SONG Huansheng. Waveletdomain dilated network for fast low-dose CT image reconstruction[J].Journal of Xidian University, 2020, 47(4): 86-93.

Figures/Tables 8

References 16

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网络结构	平均峰值信噪比/dB	平均测试时间/s
DWT + CNN+ IWT	24.7738	1.4430 / 0.1856
DWT +CNN + Subpixel	24.7763	1.4235 / 0.1749

条件	性能	LDCT	CNN10	RED-CNN	FFWDNet	FWDNet
在1mm的对比	平均RMSE	45.7471	23.5903	23.3285	23.2580	23.1206
	平均PSNR/dB	18.8504	24.6014	24.6336	24.7242	24.7763
	平均SSIM	0.6125	0.6621	0.6671	0.6713	0.6725
在3mm的对比	平均RMSE	28.6170	14.9904	15.0339	14.9627	14.8723
	平均PSNR/dB	22.9338	28.5423	28.5173	28.5589	28.6112
	平均SSIM	0.6732	0.7441	0.7425	0.7459	0.7487

运算时间	设备	CNN10	RED-CNN	FFWDNet	FWDNet
平均测试时间/s	CPU	51.36 / 2.568	91.40 / 4.570	39.00 / 1.950	39.24 / 1.962
平均测试时间/s	GPU	4.46 / 0.223	6.00 / 0.300	2.88 / 0.144	3.70 / 0.185
平均训练时间/h	GPU	12	14	10	10

Waveletdomain dilated network for fast low-dose CT image reconstruction

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