Slice-wise reconstruction for low-dose cone-beam CT using a deep residual convolutional neural network

作     者：Hong-Kai Yang Kai-Chao Liang Ke-Jun Kang Yu-Xiang Xing 

作者机构：Department of Engineering Physics Tsinghua University Key Laboratory of Particle and Radiation Imaging Ministry of Education Tsinghua University 

出 版 物：《Nuclear Science and Techniques》 (核技术（英文）)

年 卷 期：2019年第30卷第4期

页      面：53-61页

核心收录：

学科分类：08[工学] 0827[工学-核科学与技术] 

基　　金：supported by the National Natural Science Foundation of China(Nos.61771279,11435007) the National Key Research and Development Program of China(No.2016YFF0101304) 

主　　题：Cone-beam CT Slice-wise Residual U-net Low dose Image denoising 

摘      要：Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.