R\(^{2}\)-Net: Recurrent and Recursive Network for Sparse-View CT Artifacts Removal

  • Tiancheng Shen
  • Xia Li
  • Zhisheng Zhong
  • Jianlong Wu
  • Zhouchen LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


We propose a novel neural network architecture to reduce streak artifacts generated in sparse-view 2D Computed Tomography image reconstruction. This architecture decomposes the streak artifacts removal into multiple stages through the recurrent mechanism, which can fully utilize information in previous stages and guide the learning of later stages. In each recurrent stage, the key components of the architecture operate recursively. The recursive mechanism is helpful to save parameters and enlarge the receptive field efficiently with exponentially increased dilation of convolution. To verify its effectiveness, we conduct experiments on the AAPM’s CT dataset through 5-fold cross-validation. Our proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.


Computed Tomography Sparse-view reconstruction Convolutional recurrent neural network 



We thank Dr. Cynthia McCollough (the Mayo Clinic, USA) for providing CT data of Low Dose CT Grand Challenge for research purpose.

Zhouchen Lin is supported by National Basic Research Program of China (973 Program) (grant no. 2015CB352502), National Natural Science Foundation (NSF) of China (grant nos. 61625301 and 61731018), and Microsoft Research Asia.

Supplementary material

490281_1_En_36_MOESM1_ESM.pdf (3.5 mb)
Supplementary material 1 (pdf 3543 KB)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tiancheng Shen
    • 1
  • Xia Li
    • 2
  • Zhisheng Zhong
    • 2
  • Jianlong Wu
    • 2
    • 3
  • Zhouchen Lin
    • 2
    Email author
  1. 1.Center for Data SciencePeking UniversityBeijingChina
  2. 2.Key Laboratory of Machine Perception (MOE), School of EECSPeking UniversityBeijingChina
  3. 3.School of Computer Science and TechnologyShandong UniversityTsingtaoChina

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