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Abstract #2501

Fast dictionary learning-based compresssed sensing MRI with patch clustering

Zhifang Zhan 1 , Yunsong Liu 1 , Jian-Feng Cai 2 , 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) exploit the sparsity of magnetic resonance (MR) images to realize accurate reconstruction from the undersampled k-space data. Recently, there has been a growing interest in the study of adaptive sparse representation of MR images to achieve better reconstructions. However, most adaptive dictionary training processes are based on all the image patches and usually time-consuming. In this work, we proposed a fast dictionaries learning method that takes advantage of the geometric directions in classified similar patches. Experiments on T2-weighted brain image data show our proposed method improve the reconstruction quality both on reducing artifacts and minimizing reconstruction errors.

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