A deep network for sinogram and CT image reconstruction
This addresses image quality degradation in CT scans due to noise, which is a practical issue in medical imaging, but it appears incremental as it builds on existing deep learning approaches for reconstruction.
The paper tackles the problem of CT image reconstruction from noisy sinograms by designing a deep network with cascaded blocks for denoising and artifact removal, achieving the highest average PSNR and SSIM compared to state-of-the-art methods.
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.