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International Journal of Biomedical Imaging
Volume 2013, Article ID 907501, 12 pages
Research Article

Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT

1Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9
2Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, SK, Canada S7N 0W8
3School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China

Received 4 February 2013; Revised 30 April 2013; Accepted 13 May 2013

Academic Editor: Koon-Pong Wong

Copyright © 2013 Zangen Zhu 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.


Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.