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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2013-09-01
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Series: | International Journal of Distributed Sensor Networks |
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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 |
work_keys_str_mv |
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