Distributed Compressed Sensing MRI Using Volume Array Coil

The volume array coil in the magnetic resonance imaging (MRI) system is a typical application of the distributed sensor network in the biomedical area. Each coil provides a large coverage of the imaged object, and the signals are largely overlapped during the data acquisition. The intercoil image si...

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Main Authors: Zhen Feng, Feng Liu, He Guo, Zhikui Chen, Mingfeng Jiang, Mingjian Hong, Qi Jia
Format: Article
Language:English
Published: SAGE Publishing 2013-09-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2013/989678
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spelling doaj-0c381418dd3c455faf620a77a33d46562020-11-25T03:40:30ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772013-09-01910.1155/2013/989678Distributed Compressed Sensing MRI Using Volume Array CoilZhen Feng0Feng Liu1He Guo2Zhikui Chen3Mingfeng Jiang4Mingjian Hong5Qi Jia6 School of Software Technology, Dalian University of Technology, Dalian 116620, China School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia School of Software Technology, Dalian University of Technology, Dalian 116620, China School of Software Technology, Dalian University of Technology, Dalian 116620, China School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China School of Software Engineering, Chongqing University, Chongqing 400030, China School of Software Technology, Dalian University of Technology, Dalian 116620, ChinaThe volume array coil in the magnetic resonance imaging (MRI) system is a typical application of the distributed sensor network in the biomedical area. Each coil provides a large coverage of the imaged object, and the signals are largely overlapped during the data acquisition. The intercoil image similarities can be explored for the distributed compressed sensing (CS) based image reconstruction. In this work, a singular value decomposition (SVD) based sparsity basis was developed for the CS-MRI with a volume array coil configuration. In this novel imaging method, the spatial correlation both of intracoil and intercoil exploited. The experimental results showed that is with eightfold undersampled k -space data acquisition, the target images could still be faithfully reconstructed using the proposed method, which offered a better imaging performance compared to conventional CS schemes.https://doi.org/10.1155/2013/989678
collection DOAJ
language English
format Article
sources DOAJ
author Zhen Feng
Feng Liu
He Guo
Zhikui Chen
Mingfeng Jiang
Mingjian Hong
Qi Jia
spellingShingle Zhen Feng
Feng Liu
He Guo
Zhikui Chen
Mingfeng Jiang
Mingjian Hong
Qi Jia
Distributed Compressed Sensing MRI Using Volume Array Coil
International Journal of Distributed Sensor Networks
author_facet Zhen Feng
Feng Liu
He Guo
Zhikui Chen
Mingfeng Jiang
Mingjian Hong
Qi Jia
author_sort Zhen Feng
title Distributed Compressed Sensing MRI Using Volume Array Coil
title_short Distributed Compressed Sensing MRI Using Volume Array Coil
title_full Distributed Compressed Sensing MRI Using Volume Array Coil
title_fullStr Distributed Compressed Sensing MRI Using Volume Array Coil
title_full_unstemmed Distributed Compressed Sensing MRI Using Volume Array Coil
title_sort distributed compressed sensing mri using volume array coil
publisher SAGE Publishing
series International Journal of Distributed Sensor Networks
issn 1550-1477
publishDate 2013-09-01
description The volume array coil in the magnetic resonance imaging (MRI) system is a typical application of the distributed sensor network in the biomedical area. Each coil provides a large coverage of the imaged object, and the signals are largely overlapped during the data acquisition. The intercoil image similarities can be explored for the distributed compressed sensing (CS) based image reconstruction. In this work, a singular value decomposition (SVD) based sparsity basis was developed for the CS-MRI with a volume array coil configuration. In this novel imaging method, the spatial correlation both of intracoil and intercoil exploited. The experimental results showed that is with eightfold undersampled k -space data acquisition, the target images could still be faithfully reconstructed using the proposed method, which offered a better imaging performance compared to conventional CS schemes.
url https://doi.org/10.1155/2013/989678
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AT zhikuichen distributedcompressedsensingmriusingvolumearraycoil
AT mingfengjiang distributedcompressedsensingmriusingvolumearraycoil
AT mingjianhong distributedcompressedsensingmriusingvolumearraycoil
AT qijia distributedcompressedsensingmriusingvolumearraycoil
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