  A novel method for SPECT angle interpolation based on deep learning
methodologies is presented. Projection data from software phantoms were used to
train the proposed model. For evaluation of the efficacy of the method,
phantoms based on Shepp Logan, with various noise levels added were used, and
the resulting interpolated sinograms are reconstructed using Ordered Subset
Expectation Maximization (OSEM) and compared to the reconstructions of the
original sinograms. The proposed method can quadruple the projections, and
denoise the original sinogram, in the same process. As the results show, the
proposed model significantly improves the reconstruction accuracy. Finally, to
demonstrate the efficacy and capability of the proposed method results from
real-world DAT-SPECT sinograms are presented.
