  X-ray computed tomography (CT) reconstructs cross-sectional images from
projection data. However, ionizing X-ray radiation associated with CT scanning
might induce cancer and genetic damage. Therefore, the reduction of radiation
dose has attracted major attention. Few-view CT image reconstruction is an
important topic to reduce the radiation dose. Recently, data-driven algorithms
have shown great potential to solve the few-view CT problem. In this paper, we
develop a dual network architecture (DNA) for reconstructing images directly
from sinograms. In the proposed DNA method, a point-based fully-connected layer
learns the backprojection process requesting significantly less memory than the
prior arts do. Proposed method uses O(C*N*N_c) parameters where N and N_c
denote the dimension of reconstructed images and number of projections
respectively. C is an adjustable parameter that can be set as low as 1. Our
experimental results demonstrate that DNA produces a competitive performance
over the other state-of-the-art methods. Interestingly, natural images can be
used to pre-train DNA to avoid overfitting when the amount of real patient
images is limited.
