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Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()

A modified total variation MRI image denoising method is proposed in this paper. First, the proposed method removes the noise in K-space in compressed sensing MRI reconstruction. Then, the removed K-space data is used as a partial frequency observation in compressed sensing MRI model. The proposed m...

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Detalles Bibliográficos
Autores principales: Zhu, Yonggui, Shen, Weiheng, Cheng, Fanqiang, Jin, Cong, Cao, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113634/
https://www.ncbi.nlm.nih.gov/pubmed/32258499
http://dx.doi.org/10.1016/j.heliyon.2020.e03680
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author Zhu, Yonggui
Shen, Weiheng
Cheng, Fanqiang
Jin, Cong
Cao, Gang
author_facet Zhu, Yonggui
Shen, Weiheng
Cheng, Fanqiang
Jin, Cong
Cao, Gang
author_sort Zhu, Yonggui
collection PubMed
description A modified total variation MRI image denoising method is proposed in this paper. First, the proposed method removes the noise in K-space in compressed sensing MRI reconstruction. Then, the removed K-space data is used as a partial frequency observation in compressed sensing MRI model. The proposed method shows better results than RecPF method, LDP method, TVCMRI method, and FCSA method in sparse MRI reconstruction. The proposed method is tested against Shepp-Logan phantom and real MR images corrupted by noise of different intensity level, and it gives better Signal-to-Noise Ratio (SNR), the relative error (ReErr), and the structural similarity (SSIM) than RecPF, LDP, TVCMRI, and FCSA.
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spelling pubmed-71136342020-04-03 Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method() Zhu, Yonggui Shen, Weiheng Cheng, Fanqiang Jin, Cong Cao, Gang Heliyon Article A modified total variation MRI image denoising method is proposed in this paper. First, the proposed method removes the noise in K-space in compressed sensing MRI reconstruction. Then, the removed K-space data is used as a partial frequency observation in compressed sensing MRI model. The proposed method shows better results than RecPF method, LDP method, TVCMRI method, and FCSA method in sparse MRI reconstruction. The proposed method is tested against Shepp-Logan phantom and real MR images corrupted by noise of different intensity level, and it gives better Signal-to-Noise Ratio (SNR), the relative error (ReErr), and the structural similarity (SSIM) than RecPF, LDP, TVCMRI, and FCSA. Elsevier 2020-03-30 /pmc/articles/PMC7113634/ /pubmed/32258499 http://dx.doi.org/10.1016/j.heliyon.2020.e03680 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Yonggui
Shen, Weiheng
Cheng, Fanqiang
Jin, Cong
Cao, Gang
Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()
title Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()
title_full Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()
title_fullStr Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()
title_full_unstemmed Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()
title_short Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method()
title_sort removal of high density gaussian noise in compressed sensing mri reconstruction through modified total variation image denoising method()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113634/
https://www.ncbi.nlm.nih.gov/pubmed/32258499
http://dx.doi.org/10.1016/j.heliyon.2020.e03680
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AT chengfanqiang removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
AT jincong removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
AT caogang removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod