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.

翻译：在SPECT成像领域,根据创用反反转网络(GANs)提出了一种新颖的Singraphic demonisois(GANs)方法; 软件幻影的预测数据用于培训拟议的模型; 为了评价基于Shepp Logan幻影的方法的有效性,在使用的地方添加了各种噪音水平; 使用有顺序的子集成预期最大化(OSEM)和与原有名的罪恶图的重建相比,对由此产生的无名的罪恶图进行了重建。 如结果所示,拟议的方法大大缩小了罪状,大大改进了重建。 最后, 展示了真实世界DAT-SPECT罪恶图的拟议方法结果的功效和能力。