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Compressed sensing technology and its application in MRI
ZHANG Gui-shan  XIAO Gang  DAI Zhuo-zhi  SHEN Zhi-wei  LI Sheng-kai  WU Ren-hua 

DOI:10.3969/j.issn.1674-8034.2013.04.016.


[Abstract] Compressed sensing is an innovative theory of signal acquisition and processing based on the areas of applied mathematics. It works by using the mathematical algorithm to make an appropriate domain transformation for the collected signals and changing them into sparse or compressible signals. Afterwards, gathering the compressed signals directly to reconstruct the original signals at speedy, high quality by the method of the reconstruction algorithm. Due to its excellent temporal resolution advantages and with satisfactory temporal resolution, compressed sensing has become a research focus in the field of medical imaging. This article mainly elaborates in the basic theory of compressed sensing, its application in MRI and prospects for development.
[Keywords] Compressed Sensing；Fourier transformation；Magnetic resonance imaging

ZHANG Gui-shan Department of Medical Imaging, 2nd Affilicated Hospital, Shantou University Medical College, Shantou 515041, China

XIAO Gang Department of Math and Applied Mathematical, Hanshan Normal University, Chaozhou 521041, China

DAI Zhuo-zhi Department of Medical Imaging, 2nd Affilicated Hospital, Shantou University Medical College, Shantou 515041, China

SHEN Zhi-wei Department of Medical Imaging, 2nd Affilicated Hospital, Shantou University Medical College, Shantou 515041, China

LI Sheng-kai Department of Medical Imaging, 2nd Affilicated Hospital, Shantou University Medical College, Shantou 515041, China

WU Ren-hua* Department of Medical Imaging, 2nd Affilicated Hospital, Shantou University Medical College, Shantou 515041, China; Provincial Key Laboratory of Medical Molecular Imaging, Guangdong, Medical College of Shantou University, Shantou 515041, China

*Correspondence to: Wu RH, Email: cjr.wurenhua@vip.163.com

Conflicts of interest   None.

Received  2012-12-27
Accepted  2013-04-09
DOI: 10.3969/j.issn.1674-8034.2013.04.016
DOI:10.3969/j.issn.1674-8034.2013.04.016.

[1]
Shannon CE. Communication in the presence of noise. Proceedings IEEE, 1949, 37: 10-21.
[2]
Candès EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. Information Theory, IEEE Transactions, 2006, 52(2): 489-509.
[3]
Donoho D L. Compressed sensing. Information Theory, IEEE Transactions, 2006, 52(4): 1289-1306.
[4]
Baraniuk RG. Compressive sensing [lecture notes]. Signal Processing Magazine IEEE, 2007, 24(4): 118-121.
[5]
Candès EJ, Wakin MB. An introduction to compressive sampling. Signal Processing Magazine IEEE, 2008, 25(2): 21-30.
[6]
Kutyniok G. Compressed sensing: theory and applications. arXiv, 2012: 1203, 3815.
[7]
杨晓兰,朱永贵,丛佳,等.基于压缩感知的核磁共振图像重建的Bregman方法.中国传媒大学学报:自然科学版, 2012, 19(4): 18-26.
[8]
冯晓源,刘含秋.功能磁共振成像在中国的研究现状.中国医学计算机成像杂志, 2004, 10(5): 292-296.
[9]
Jung H, Ye JC. Performance evaluation of accelerated functional MRI acquisition using compressed sensing. Biomedical Imaging: from nano to macro, 2009. IEEE, 2009: 702-705.
[10]
Vaswani N. Kalman filtered compressed sensing. Image Processing, 2008. IEEE, 2008: 893-896.
[11]
Vasanawala SS, Alley MT, Hargreaves BA, et al. Improved pediatric MR imaging with compressed sensing1. Radiology, 2010, 256(2): 607-616.
[12]
刘晓芳,叶修梓,张三元,等.基于变量分裂法的稀疏约束并行磁共振图像重建.模式识别与人工智能, 2013, 26(1): 6-13.
[13]
Pang Y, Zhang X. Interpolated compressed sensing for 2D Multiple slice fast MR imaging. PloS one, 2013, 8(2): e56098
[14]
Chartrand R. Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data. Biomedical Imaging: from nano to macro, 2009. IEEE, 2009: 262-265.
[15]
Ardenkjaer-Larsen JH, Fridlund B, Gram A, et al. Increase in signal-to-noise ratio of > 10 000 times in liquid-state NMR. Proc Natl Acad Sci U S A. 2003, 100(18): 10158-10163.
[16]
Goertz ST, Harmsen J, Heckmann J, et al. Highest polarizations in deuterated compounds. Nuclear instruments and methods in physics research section A, 2004, 526(1-2): 43-52.
[17]
Cunningham CH, Chen AP, Albers MJ, et al. Double spin-echo sequence for rapid spectroscopic imaging of hyperpolarized 13C. J Magn Reson Imag, 2007, 187(2): 357-362.
[18]
Cunningham CH, Vigneron DB, Chen AP, et al. Design of flyback echo-planar readout gradients for magnetic resonance spectroscopic imaging. Magn Reson Med, 2005, 54(5): 1286-1289.
[19]
Hu S, Lustig M, Chen AP, et al. Compressed sensing for resolution enhancement of hyperpolarized 13C flyback 3D-MRSI. J Magnetic Reson Imag, 2008, 192(2): 258-264.
[20]
Larson PE, Hu S, Lustig M, et al. Fast dynamic 3D MR spectroscopic imaging with compressed sensing and multiband excitation pulses for hyperpolarized 13C studies. Magn Reson Med, 2011, 65(3): 610-619.
[21]
Wu Y, Wu EX. MR study of postnatal development of myocardial structure and left ventricular function. J Magn Reson Imag, 2009, 30(1): 47-53.
[22]
Fan X, Markiewicz EJ, Zamora M, et al. Comparison and evaluation of mouse cardiac MRI acquired with open birdcage, single loop surface and volume birdcage coils. Physic med biology, 2006, 51(24): N451.
[23]
Wech T, Lemke A, Medway D, et al. Accelerating cine-MR imaging in mouse hearts using compressed sensing. J Magn Reson Imag, 2011, 34(5): 1072-1079.
[24]
Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. Tennessee: Gatesmark Publishing, 2009: 126-212.
[25]
杨正汉，冯逢，王霄英.磁共振成像技术指南.北京:人民军医出版社, 2010: 173-179
[26]
Lustig M, Alley M, Vasanawala S, et al. L1 SPIR-iT: autocalibrating parallel imaging compressed sensing. Proc Intl Soc Mag Reson Med, 2009, 17: 379.
[27]
Lustig M, Donoho DL, Santos JM, et al. Compressed sensing MRI. Signal Processing Magazine IEEE, 2008, 25(2): 72-82.
[28]
Otazo R, Kim D, Axel L, et al. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med, 2010, 64(3): 767-776.
[29]
Lustig M, Santos JM, Donoho DL, et al. kt SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. Seattle: Proceedings of the 13th Annual Meeting of ISMRM, 2006: 2420.
[30]
Tsao J, Boesiger P, Pruessmann KP. K-t BLAST and k-t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med, 2003, 50(5): 1031-1042.
[31]
Li F, Ricardo O, Hong J. Accelerated Cardiac T2 Mapping using Breath-hold Multi-Echo Fast Spin-Echo Pulse Sequence with Compressed sensing and Parallel Imaging. Magn Reson Med, 2011, 65(6): 1661-1669.
[32]
Kim D, Jensen JH, Wu EX, et al. Breathhold multiecho fast spin-echo pulse sequence for accurate R2 measurement in the heart and liver. Magn Reson Med, 2009, 62(2): 300-303
[33]
Jung H, Ye JC, Kim EY. Improved k-t BLAST and k-t SENSE using FOCUSS. Phys Med Biol, 2007, 52(11): 3201-3226.

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