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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