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

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posted on 14.05.2018, 14:18 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.

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Citation

Scientific Reports, 2018, 8 (6700)

Author affiliation

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

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VoR (Version of Record)

Published in

Scientific Reports

Publisher

Nature Publishing Group

eissn

2045-2322

Acceptance date

12/04/2018

Copyright date

2018

Available date

14/05/2018

Publisher version

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

Language

en

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