Learned Full-Sampling Reconstruction

X-ray computed tomography (CT) reconstruction with sparse projection views was proposed to reduce both the radiation dose and scan time. However, lacking of sufficient projection views may lead to severe artifacts for analytical reconstruction method such as the filtered back projection (FBP). Although the projection data is incomplete, we can generate the full-sampling system matrices according to the sufficient-sampling conditions [5]. Thus, we propose a novel iterative reconstruction model to fit the target images and the corresponding high resolution measurements in Radon domain by the full-sampling system matrices. Our proposed model is solved by the learned alternating minimization method, which accounts for a forward operator in deep neural network by the unrolling strategy. Numerical results demonstrate that the proposed approach outperforms some latest learning based reconstruction methods for the sparse-view CT problems.