Accelerating sensitivity encoding using compressed sensing.
The combination of Compressed Sensing (CS) and SENSitivity Encoding (SENSE) for improving MRI acquisition speed and robustness has recently drawn great attentions. However, in the direct combination, the encoding matrix which represents the Fourier transform of channel-specific sensitivity modulation is not guaranteed to be a good CS matrix. In this paper, we propose a different approach that applies CS and SENSE sequentially. The method first uses CS to reconstruct a set of aliased images in each coil, and then applies the basic SENSE on these images to reconstruct the final image. The total reduction factor can achieve the product of the factors of each individual method. The experimental results show that overall performance of our proposed method is superior to the direct combination method with the same reduction factor.
Liang
Dong
D
Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, 53201 USA. liangd@uwm.edu
Liu
Bo
B
Ying
Leslie
L
D001698:Biomedical Engineering; D001921:Brain / Q000033:anatomy & histology; D044962:Data Compression / Q000379:methods* / Q000706:statistics & numerical data; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity