title: Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT.
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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9.
authors.forename: Zangen
authors.initials: Z
authors.lastname: Zhu
authors.forename: Khan
authors.initials: K
authors.lastname: Wahid
authors.forename: Paul
authors.initials: P
authors.lastname: Babyn
authors.forename: Ran
authors.initials: R
authors.lastname: Yang