A CT image can be well reconstructed when the sampling rate of the sinogram satisfies the Nyquist criteria and the sampled signal is noise-free. However, in practice, the sinogram is usually contaminated by noise, which degrades the quality of a reconstructed CT image. In this paper, we design a deep network for sinogram and CT image reconstruction. The network consists of two cascaded blocks that are linked by a filter backprojection (FBP) layer, where the former block is responsible for denoising and completing the sinograms while the latter is used to removing the noise and artifacts of the CT images. Experimental results show that the reconstructed CT images by our methods have the highest PSNR and SSIM in average compared to state of the art methods.

中文翻译：

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用于正弦图和 CT 图像重建的深度网络

当正弦图的采样率满足奈奎斯特准则且采样信号无噪声时，可以很好地重建CT图像。然而，在实践中，正弦图通常被噪声污染，这会降低重建 CT 图像的质量。在本文中，我们设计了一个用于正弦图和 CT 图像重建的深度网络。该网络由两个级联块组成，它们由滤波器反投影 (FBP) 层链接，其中前一个块负责去噪和完成正弦图，而后者用于去除 CT 图像的噪声和伪影。实验结果表明，与最先进的方法相比，我们的方法重建的 CT 图像平均具有最高的 PSNR 和 SSIM。