论文标题
正式和CT图像重建的深层网络
A deep network for sinogram and CT image reconstruction
论文作者
论文摘要
当Sinogram的采样速率满足Nyquist标准并且采样信号无噪声时，CT图像可以很好地重构。但是，在实践中，正弦图通常被噪声污染，这会降低重建的CT图像的质量。在本文中，我们为正式和CT图像重建设计了一个深层网络。该网络由两个级联块组成，这些块由滤波器反射（FBP）层链接，其中前一个块负责降解和完成正式图，而后者则用于消除CT图像的噪声和伪影。实验结果表明，与最先进的方法相比，通过我们的方法重建的CT图像平均具有最高的PSNR和SSIM。
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.
