Compressed sensing-based MRI reconstruction using complex double-density dual-tree DWT

Authors:
Zangen Zhu;Khan Wahid;Paul Babyn;Ran Yang
Affiliations:
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Department of Medical Imaging, University of Saskatchewan and Saskatoon Health Region, Saskatoon, SK, Canada;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, Guangdong, China
Venue:
Journal of Biomedical Imaging
Year:
2013

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Abstract

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.