关键词: 低剂量计算机断层扫描, 卷积神经网络, 残差学习, 深度学习

Abstract: A new algorithm, Deep Residual Convolutional Neural Network（DR-CNN）, is proposed to remove streak artifacts within the reconstructed image of Low-Dose Computed Tomography（LDCT）, which can estimate Normal-Dose Computed Tomography（NDCT） image from LDCT image. In training phase, the residual images are obtained by subtracting LDCT images, LDCT images are taken as inputs, NDCT images are taken as labels, and the mapping relationship between them can be learned by a deep Convolutional Neural Network（CNN）. In test phase, the residual image can be estimated from an LDCT image using the mapping relationship learning in training phase, and the estimated NDCT image can be obtained by subtracting the residual image from the LDCT image. Fifty pairs of normal-dose thoracic scan sections of the same phantom and reconstructed images with noises added to the projection field are used as data sets, of which 45 pairs are used as training sets and others are used as test sets to verify the validity of this algorithm. Compared with the-state-of-art methods, such as non-local means, block-matching and 3D filtering and K-SVD algorithms, the estimated NDCT image has a smaller value of the Root Mean Square Error（RMSE） and a higher Peak Signal-to-Noise Ratio（PSNR） than other algorithms.

Key words: low-dose computed tomography, convolutional neural network, residual learning, deep learning