Sparse-view CT image restoration via multiscale wavelet residual network

Abstract

Sparse-view CT has the advantages of accelerated data collection and reduced radiation dose, but data missing arising from the data collection process causes serious streaking artifact and noise in the images reconstructed using the traditional filtering back projection algorithm (FBP). To solve this problem, we propose a multi-scale wavelet residual network (MWResNet) to restore sparse-view CT images. The MWResNet was based on the combination of deep learning and traditional model in MWCNN, and the wavelet network was combined with the residual block to enhance the network's ability to embed image features and speed up network training. The network proposed herein was trained using the real spiral geometry CT image data, namely the Low-dose CT Grand Challenge dataset. The results of the proposed networks were visually and quantitatively compared to that by other existing networks, including the image restoration iterative residual convolution network (IRLNet), residual coding-decoding convolutional neural network (REDCNN) and the FBP convolutional neural network (FBPConvNet). The results demonstrated that the proposed method was superior to other competing methods in terms of visual inspection and quantitative comparison. The MWResNet network is an effective method for suppressing noise and artifacts and maintaining edges details in the sparse-view CT images.