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

SPECT Angle Interpolation Based on Deep Learning Methodologies