University of Leicester
Browse
s41598-018-25153-w.pdf (2.31 MB)

Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction.

Download (2.31 MB)
journal contribution
posted on 2018-05-14, 14:18 authored by Shipeng Xie, Xinyu Zheng, Yang Chen, Lizhe Xie, Jin Liu, Yudong Zhang, Jingjie Yan, Hu Zhu, Yining Hu
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.

History

Citation

Scientific Reports, 2018, 8 (6700)

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

  • VoR (Version of Record)

Published in

Scientific Reports

Publisher

Nature Publishing Group

eissn

2045-2322

Acceptance date

2018-04-12

Copyright date

2018

Available date

2018-05-14

Publisher version

https://www.nature.com/articles/s41598-018-25153-w

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    Keywords

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC