Distributed Compressed Sensing MRI Using Volume Array Coil
Author(s) -
Zhen Feng,
Feng Liu,
He Guo,
Zhikui Chen,
Mingfeng Jiang,
Mingjian Hong,
Qi Jia
Publication year - 2013
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2013/989678
Subject(s) - computer science , compressed sensing , electromagnetic coil , volume (thermodynamics) , singular value decomposition , magnetic resonance imaging , iterative reconstruction , data acquisition , artificial intelligence , real time mri , computer vision , physics , medicine , quantum mechanics , radiology , operating system
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. © 2013 Zhen Feng et al.
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