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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 985819, 16 pages
Research Article

MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

1Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620015, India
2Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
3Department of Medical Imaging, Royal University Hospital, University of Saskatchewan, Saskatoon, SK, Canada

Received 26 April 2013; Revised 26 June 2013; Accepted 11 July 2013

Academic Editor: Dimitri Van De Ville

Copyright © 2013 Varun P. Gopi 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.


This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled -space data. The nonlocal total variation is taken as the -regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.