Sparse-View CT Reconstruction Based on Improved Re-Sidual Network

  • Yufei QianEmail author
  • Shipeng Xie
  • Wenqin Zhuang
  • Haibo Li
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 75)


With the development of CT imaging, people have higher requirements for the quality of CT image reconstruction. It is desirable to use as low as reasonably achievable X-ray dose while meeting the quality of imaging requirements. Sparse-view reconstruction is a valid measure to resolve the radiation dose problem. Owing to the angular range of projection data does not satisfy the data completeness condition, sparse-view reconstruction has always been a conundrum in CT image reconstruction. In this paper, we introduces a new CT sparse-view reconstruction algorithm, which bases on the residual network. We optimize traditional residual models by improving the superfluous modules and reducing unnecessary calculations. Compared to several other classic methods, the experimental results with our network obtained better consequent, regarding artifact reduction, feature preservation, and computational speed.


CT image reconstruction Sparse-view Residual network 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yufei Qian
    • 1
    Email author
  • Shipeng Xie
    • 1
  • Wenqin Zhuang
    • 1
  • Haibo Li
    • 1
    • 2
  1. 1.College of Telecommunications & Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Department of Media Technology and Interaction Design, School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholmSweden

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