Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction

Abstract 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 de...

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Autores principales:	Shipeng Xie, Xinyu Zheng, Yang Chen, Lizhe Xie, Jin Liu, Yudong Zhang, Jingjie Yan, Hu Zhu, Yining Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b8cc37968daf44898d6e86aee02c35ce
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id	oai:doaj.org-article:b8cc37968daf44898d6e86aee02c35ce
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spelling	oai:doaj.org-article:b8cc37968daf44898d6e86aee02c35ce2021-12-02T12:32:35ZArtifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction10.1038/s41598-018-25153-w2045-2322https://doaj.org/article/b8cc37968daf44898d6e86aee02c35ce2018-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25153-whttps://doaj.org/toc/2045-2322Abstract 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.Shipeng XieXinyu ZhengYang ChenLizhe XieJin LiuYudong ZhangJingjie YanHu ZhuYining HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shipeng Xie Xinyu Zheng Yang Chen Lizhe Xie Jin Liu Yudong Zhang Jingjie Yan Hu Zhu Yining Hu Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
description	Abstract 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.
format	article
author	Shipeng Xie Xinyu Zheng Yang Chen Lizhe Xie Jin Liu Yudong Zhang Jingjie Yan Hu Zhu Yining Hu
author_facet	Shipeng Xie Xinyu Zheng Yang Chen Lizhe Xie Jin Liu Yudong Zhang Jingjie Yan Hu Zhu Yining Hu
author_sort	Shipeng Xie
title	Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
title_short	Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
title_full	Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
title_fullStr	Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
title_full_unstemmed	Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction
title_sort	artifact removal using improved googlenet for sparse-view ct reconstruction
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/b8cc37968daf44898d6e86aee02c35ce
work_keys_str_mv	AT shipengxie artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT xinyuzheng artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT yangchen artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT lizhexie artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT jinliu artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT yudongzhang artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT jingjieyan artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT huzhu artifactremovalusingimprovedgooglenetforsparseviewctreconstruction AT yininghu artifactremovalusingimprovedgooglenetforsparseviewctreconstruction
_version_	1718394058057973760

Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction

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