Abstract #3423
Balanced sparse MRI model: Bridge the analysis and synthesis sparse models in compressed sensing MRI
Yunsong Liu 1 , Jian-Feng Cai 2 , Zhifang Zhan 1 , Di Guo 3 , Jing Ye 1 , Zhong Chen 1 , and Xiaobo Qu 1
1
Department of Electronic Science, Xiamen
University, Xiamen, Fujian, China,
2
Department
of Mathematics, University of Iowa, Iowa City, Iowa,
United States,
3
School
of Computer and Information Engineering, Xiamen
University of Technology, Xiamen, Fujian, China
Compressed sensing (CS) has shown to be promising to
accelerate magnetic resonance imaging (MRI). There are
two different sparse models in CS-MRI: analysis and
synthesis models with different assumptions and
performance when a redundant tight frame is used. A new
balance model is introduced into CS-MRI that can achieve
the solutions of the analysis model, synthesis model and
some in between by tuning the balancing parameter. It is
found in this work that the typical balance model has a
comparable performance with the analysis model in
CS-MRI. Both of them achieve lower reconstructed errors
than the synthesis model no matter what value the
balancing parameter is. These observations are
consistent for different tight frames used CS-MRI.
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