Paper Info
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.
Year
DOI
Venue
2013
10.1155/2013/907501
Int. J. Biomedical Imaging
Keywords
Field
DocType
double-density dual-tree discrete wavelet,mri method,signal sampling,k-space data,signal acquisition,sensing-based mri reconstruction,sensing-based reconstruction method,complex double-density dual-tree,nyquist-shannon sampling theorem,aliasing artifact,data acquisition time,sparse representation,bioinformatics,biomedical research
Data mining,Computer science,Artificial intelligence,Discrete wavelet transform,Nyquist–Shannon sampling theorem,Compressed sensing,Wavelet transform,Computer vision,Pattern recognition,Sparse approximation,Data acquisition,Undersampling,Aliasing
Journal
Volume
ISSN
Citations 
2013
1687-4188
10
PageRank 
References 
Authors
0.64
9
4
Name
Order
Citations
PageRank
Zangen Zhu1222.37
Khan Wahid2263.70
Paul Babyn316621.42
Ran Yang4919.74