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Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction

Xie, Shipeng ; Zheng, Xinyu ; Chen, Yang ; Xie, Lizhe ; Liu, Jin ; Zhang, Yudong ; Yan, Jingjie ; Zhu, Hu ; Hu, Yining

Scientific reports, 2018-04, Vol.8 (1), p.6700-9, Article 6700 [Periódico revisado por pares]

England: Nature Publishing Group

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  • Título:
    Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
  • Autor: Xie, Shipeng ; Zheng, Xinyu ; Chen, Yang ; Xie, Lizhe ; Liu, Jin ; Zhang, Yudong ; Yan, Jingjie ; Zhu, Hu ; Hu, Yining
  • Assuntos: Algorithms ; Architecture ; Communications networks ; Computed tomography ; Deep learning ; Geometry ; Image processing ; Laboratories ; Noise ; Radiation
  • É parte de: Scientific reports, 2018-04, Vol.8 (1), p.6700-9, Article 6700
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.
  • Editor: England: Nature Publishing Group
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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