SPECT Angle Interpolation Based on Deep Learning Methodologies
This work addresses a domain-specific problem in medical imaging by providing an incremental improvement for SPECT reconstruction.
The paper tackles the problem of SPECT angle interpolation by introducing a deep learning method that quadruples projections and denoises sinograms simultaneously, showing significant improvement in reconstruction accuracy on both software phantoms and real-world DAT-SPECT data.
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.