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.

翻译：介绍了基于深层学习方法的SPECT角度内插新方法。软件幻影的预测数据被用于培训拟议的模型。为了评价该方法的功效,使用了基于Shep Logan的幻影,并增加了各种噪音水平,并使用有顺序的子预期最大化(OSEM)和与原正弦图的重建相比,对由此形成的内插罪恶图进行了重建。拟议的方法可以使预测翻四番,并在同一过程中将原正弦图改写下来。结果显示,拟议的模型大大提高了重建的准确性。最后,还介绍了真实世界DAT-SPECT罪谱图的拟议方法结果的功效和能力。