关键词: 稀疏重建, 条状伪影, 卷积神经网络, UNet, 多残差UNet

Abstract: Aiming at the problem of producing streak artifacts during sparse analytic reconstruction of Computed Tomography (CT), in order to better suppress strip artifacts, a Multiply residual UNet (Mr-UNet) network architecture was proposed based on the classical UNet network architecture. Firstly, the sparse images with streak artifacts were sparsely reconstructed by the traditional Filtered Back Projection (FBP) analytic reconstruction algorithm. Then, the reconstructed images were used as the input of the network structure, and the corresponding high-precision images were trained as the labels of the network, so that the network had a good performance of suppressing streak artifacts. Finally, the original four-layer down-sampling of the classical residual UNet was deepened to five layers, and the residual learning mechanism was introduced into the proposed model, so that each convolution unit was constructed to residual structure to improve the training performance of the network. In the experiments, 2 000 pairs of images containing images with streak artifacts and the corresponding high-precision images with the size of 256×256 were used as the dataset, among which, 1 900 pairs were used as the training set, 50 pairs were used as the verification set, and the rest were used as the test set to train the network, and verify and evaluate the network performance. The experimental results show that, compared with the traditional Total Variation (TV) minimization algorithm and the classical deep learning method of UNet, the proposed model can reduce the Root Mean Square Error (RMSE) by about 0.002 5 on average and improve the Structural SIMilarity (SSIM) by about 0.003 on average, and can retain the texture and detail information of the image better.

Key words: sparse reconstruction, streak artifact, Convolutional Neural Network (CNN), UNet, Multiply residual UNet