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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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