A deep network for sinogram and CT image reconstruction

• URL: http://arxiv.org/abs/2001.07150v1
• Date: Mon, 20 Jan 2020 15:50:16 GMT
• Title: A deep network for sinogram and CT image reconstruction
• Authors: Wei Wang, Xiang-Gen Xia, Chuanjiang He, Zemin Ren, Jian Lu, Tianfu Wang and Baiying Lei
• Abstract summary: 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 layer. Experimental results show that the reconstructed CT images have the highest PSNR and SSIM in average compared to state of the art methods.
• Score: 28.175533839713847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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