  A CT image can be well reconstructed when the sampling rate of the sinogram
satisfies the Nyquist criteria and the sampled signal is noise-free. However,
in practice, the sinogram is usually contaminated by noise, which degrades the
quality of a reconstructed CT image. In this paper, we design a deep network
for sinogram and CT image reconstruction. The network consists of two cascaded
blocks that are linked by a filter backprojection (FBP) layer, where the former
block is responsible for denoising and completing the sinograms while the
latter is used to removing the noise and artifacts of the CT images.
Experimental results show that the reconstructed CT images by our methods have
the highest PSNR and SSIM in average compared to state of the art methods.
