Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (23): 209-215.DOI: 10.3778/j.issn.1002-8331.1810-0069

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Low-Dose CT Restoration Based on CNN in NSST Domain

LIU Yi, GAO Jingzhi, GUI Zhiguo   

  1. Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Taiyuan 030051, China
  • Online:2019-12-01 Published:2019-12-11



  1. 中北大学 山西省生物医学成像与影像大数据重点实验室,太原 030051

Abstract: In order to solve the noise/artifact problem in Low-Dose Computed Tomography(LDCT) images, a Convolution Neural Network(CNN) based on Non-Sample Shearlet Transformation(NSST), NSST-CNN model, is proposed in this paper. During training, NSST decomposition is performed on Normal-Dose Computed Tomography(NDCT) and LDCT images in the data set. The high-frequency sub-images of LDCT are used as the input, and the residual images of the high-frequency sub-images of NDCT and LDCT are used as the label. The mapping relationship between sub-images of LDCT and high-frequency sub-images of the residual images is then learned through CNN training. When testing, the high-frequency sub-images of LDCT are subtracted from the trained noise/artifact and the inverse NSST transform is performed to obtain a high-quality LDCT image. Experimental results show that NSST-CNN achieves a better balance between suppressing artifacts/noise and protecting structural details than KSVD, BM3D, and image space CNN method.

Key words: low-dose Computed Tomography(CT), image restoration, non-sample Shearlet transformation, convolutional neural network, residual learning

摘要: 为解决低剂量CT(Low-Dose Computed Tomography,LDCT)图像中的噪声/伪影问题,提出一种基于非下采样Shearlet变换(Non-Sample Shearlet Transformation,NSST)的卷积神经网络(Convolution Neural Network,CNN)的NSST-CNN模型。训练时,对数据集中的常规剂量CT(Normal-Dose Computed Tomography,NDCT)和LDCT图像做NSST分解,将LDCT图像的高频子图作为输入,LDCT和NDCT图像的高频子图的残差图像作为标签,通过CNN训练,学习LDCT高频子图和高频残差子图的映射关系;测试时,将LDCT图像的高频子图减去利用映射关系预测的主要包括噪声/伪影的高频子图,然后做NSST反变换得到高质量的LDCT图像。实验结果表明,与KSVD、BM3D以及图像域CNN方法相比,NSST-CNN模型得到的结果具有更高的峰值信噪比和结构相似度,更接近NDCT图像。

关键词: 低剂量CT, 图像恢复, 非下采Shearlet变换, 卷积神经网络, 残差学习