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

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