Toeplitz random encoding MR imaging using compressed sensing

Authors:
Dong Liang;Guangwu Xu;Haifeng Wang;Kevin F. King;Dan Xu;Leslie Ying
Affiliations:
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI;Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI;Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI;Global Applied Science Lab, GE Healthcare, Waukesha, WI;Global Applied Science Lab, GE Healthcare, Waukesha, WI;Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

Citing 3
Cited 0

Toeplitz-Structured Compressed Sensing Matrices

SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

IEEE Transactions on Information Theory
Compressed sensing

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Compressed Sensing (CS), as a new framework for data acquisition and signal recovery, has been applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. However, Fourier encoding is not universal and weakly spreads out the energy of most natural images. This limits the achievable reduction factors. In this paper, we propose a Toeplitz random encoding method that is universal and spreads out the image energy more evenly. The MR physical feasibility of the proposed encoding method is verified by Bloch simulation, and the superior performance of the proposed method is demonstrated in simulation results.