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

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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Bibliographic Details
Main Authors: Yonggui Zhu, Weiheng Shen, Fanqiang Cheng, Cong Jin, Gang Cao
Format: Article
Language:English
Published: Elsevier 2020-03-01
Series:Heliyon
Subjects:
Medical imaging
Mathematics
Total variation denoising
K-space data
MRI reconstruction
Compressed sensing
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844020305259
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spelling doaj-1564881c34e74d99a3762b475754bd0e2020-11-25T02:07:42ZengElsevierHeliyon2405-84402020-03-0163e03680Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising methodYonggui Zhu0Weiheng Shen1Fanqiang Cheng2Cong Jin3Gang Cao4School of Data Science and Media Intelligence, Communication University of China, Beijing, 100024, China; Corresponding author.School of Data Science and Media Intelligence, Communication University of China, Beijing, 100024, ChinaSchool of Data Science and Media Intelligence, Communication University of China, Beijing, 100024, ChinaSchool of Information and Communication Engineering, Communication University of China, Beijing, 100024, ChinaSchool of Computer Science and Cybersecurity, Communication University of China, Beijing, 100024, ChinaA 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.http://www.sciencedirect.com/science/article/pii/S2405844020305259Medical imagingMathematicsTotal variation denoisingK-space dataMRI reconstructionCompressed sensing
collection DOAJ
language English
format Article
sources DOAJ
author Yonggui Zhu
Weiheng Shen
Fanqiang Cheng
Cong Jin
Gang Cao
spellingShingle Yonggui Zhu
Weiheng Shen
Fanqiang Cheng
Cong Jin
Gang Cao
Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method
Heliyon
Medical imaging
Mathematics
Total variation denoising
K-space data
MRI reconstruction
Compressed sensing
author_facet Yonggui Zhu
Weiheng Shen
Fanqiang Cheng
Cong Jin
Gang Cao
author_sort Yonggui Zhu
title 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_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_sort removal of high density gaussian noise in compressed sensing mri reconstruction through modified total variation image denoising method
publisher Elsevier
series Heliyon
issn 2405-8440
publishDate 2020-03-01
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.
topic Medical imaging
Mathematics
Total variation denoising
K-space data
MRI reconstruction
Compressed sensing
url http://www.sciencedirect.com/science/article/pii/S2405844020305259
work_keys_str_mv AT yongguizhu removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
AT weihengshen removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
AT fanqiangcheng removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
AT congjin removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
AT gangcao removalofhighdensitygaussiannoiseincompressedsensingmrireconstructionthroughmodifiedtotalvariationimagedenoisingmethod
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