Sinogram Denoise Based on Generative Adversarial Networks
  A novel method for sinogram denoise based on Generative Adversarial Networks
(GANs) in the field of SPECT imaging is presented. Projection data from
software phantoms were used to train the proposed model. For evaluation of the
efficacy of the method Shepp Logan based phantom, with various noise levels
added where used. The resulting denoised sinograms are reconstructed using
Ordered Subset Expectation Maximization (OSEM) and compared to the
reconstructions of the original noised sinograms. As the results show, the
proposed method significantly denoise the sinograms and significantly improves
the reconstructions. Finally, to demonstrate the efficacy and capability of the
proposed method results from real-world DAT-SPECT sinograms are presented.
