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Computational and Mathematical Methods in Medicine - Volume 2014 2014, Article ID 257435, 6 pages -

Research Article

Departments of Communication Engineering and Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China

School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China

Received 1 April 2014; Accepted 27 April 2014; Published 20 May 2014

Academic Editor: Peng Feng

Copyright © 2014 Qiyue Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Magnetic resonance imaging has been benefited from compressed sensing in improving imaging speed. But the computation time of compressed sensing magnetic resonance imaging CS-MRI is relatively long due to its iterative reconstruction process. Recently, a patch-based nonlocal operator PANO has been applied in CS-MRI to significantly reduce the reconstruction error by making use of self-similarity in images. But the two major steps in PANO, learning similarities and performing 3D wavelet transform, require extensive computations. In this paper, a parallel architecture based on multicore processors is proposed to accelerate computations of PANO. Simulation results demonstrate that the acceleration factor approaches the number of CPU cores and overall PANO-based CS-MRI reconstruction can be accomplished in several seconds.

Autor: Qiyue Li, Xiaobo Qu, Yunsong Liu, Di Guo, Jing Ye, Zhifang Zhan, and Zhong Chen



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