Share:
Share this content in WeChat
X
Technical Article
The research of influence between different incoherent sampling patterns and point spread functions in MRI
LIU Qing  LING Yong-quan  KUANG Wei-chao  LI Ya 

DOI:10.12015/issn.1674-8034.2016.10.012.


[Abstract] Compressed sensing (CS), which is a new theory that emphasizes reducing sampling data at the source, is regarded as the most promising technique in fast magnetic resonance imaging (MRI). How to evaluate the incoherence of compressed sensing-magnetic resonance imaging (CS-MRI) accurately is a key point to design the incoherent sampling track in MRI. The existing incoherence evaluation indices still follow those used in CS. They ignore the practical influence of the magnetic resonance devices so the practical performance of these incoherence evaluation indices is much different from that in theory when CS is applied to MRI. The problem is like a "barrier" between CS and MRI and restricts the performance in CS-MRI. The paper proposes to convert the transform point spread function (TPSF) to point spread function (PSF). Therefore, the mathematical relationships between the PSFs and sampling trajectory are formulated. Further, the relationships between the positions of sampling points and the shapes of PSFs are also given. At last, simulation experiments are taken to test PSFs in different sampling modes. Simulation results show that except the width of main lobes and the height of side lobe, the distribution characteristics of side lobes have a major effect on the shapes of the PSF.
[Keywords] Compressed sensing;Magnetic resonance imaging;Incoherent sampling

LIU Qing School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China

LING Yong-quan School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China

KUANG Wei-chao School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China

LI Ya* School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China

*Correspondence to: Li Y, E-mail: liya2829@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Natural Science Foundation of China No. 61372173
Received  2016-05-31
Accepted  2016-09-04
DOI: 10.12015/issn.1674-8034.2016.10.012
DOI:10.12015/issn.1674-8034.2016.10.012.

[1]
Candès EJ, Romberg J, Tao T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. Information Theory, IEEE Transactions on, 2006, 52(2): 489-509.
[2]
Donoho DL. Compressed sensing. Information Theory, IEEE Transactions on, 2006, 52(4): 1289-1306.
[3]
Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 2007, 58(6): 1182-1195.
[4]
张桂珊,肖刚,戴卓智,等.压缩感知技术及其在MRI上的应用.磁共振成像, 2013, 4(4): 314-320.
[5]
刘玉,崔皓然,粘永健,等.医学图像无损压缩技术研究进展.磁共振成像, 2016, 7(2): 149-155.
[6]
Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. The British Journal of Radiology, 2015, 88(1056): 20150487.
[7]
Elad M. Sparse and redundant representation modeling-What next?. Signal Processing Letters, IEEE, 2012, 19(12): 922-928.
[8]
Strohmer T. Measure what should be measured: progress and challenges in compressive sensing. Signal Processing Letters, IEEE, 2012, 19(12): 887-893.
[9]
戴琼海,付长军,季向阳.压缩感知研究.计算机学报, 2011, 34(3):425-434.
[10]
石光明,刘丹华,高大化,等.压缩感知理论及其研究进展.电子学报, 2009, 37(5): 1070-1081.
[11]
Jones A, Adcock B, Hansen A. On asymptotic incoherence and its implications for compressed sensing of inverse problems. Information Theory, IEEE Transactions on, 2016, 62(2): 1020-1037.
[12]
Haldar JP, Hernando D, Liang ZP. Compressed-sensing MRI with random encoding. Medical Imaging, IEEE Transactions on, 2011,30(4): 893-903.
[13]
Wang H, Liang D, King KF, et al. Three-dimensional hybrid-encoded MRI using compressed sensing//Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on. IEEE, 2012: 398-401.
[14]
Liang D, Xu G, Wang H, et al. Toeplitz random encoding MR imaging using compressed sensing//Biomedical Imaging: From Nano to Macro, 2009. ISBI'09. IEEE International Symposium on. IEEE, 2009:270-273.
[15]
Puy G, Marques JP, Gruetter R, et al. Spread spectrum magnetic resonance imaging. Medical Imaging, IEEE Transactions on, 2012,31(3): 586-598.
[16]
Pawar K, Egan G, Zhang J. Multichannel compressive sensing MRI using noiselet encoding. PloS one, 2015, 10(5): e0126386.
[17]
许志强.压缩感知.中国科学:数学, 2012, 42(9): 865-877.
[18]
Krahmer F, Ward R. Stable and robust sampling strategies for compressive imaging. Image Processing, IEEE Transactions on, 2014,23(2): 612-622.
[19]
Krahmer F, Rauhut H, Ward R. Local coherence sampling in compressed sensing//Proceedings of the 10th International Conference on Sampling Theory and Applications, July 1st-July 5th. 2013: 476-480.
[20]
Rauhut H, Ward R. Sparse Legendre expansions via l1-minimization. Journal of approximation theory, 2012, 164(5): 517-533.
[21]
Adcock B, Hansen A, Roman B, et al. Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum. Advances in Electronics and Electron Physics, 2014, 182:187-279.
[22]
Adcock B, Hansen AC, Poon C, et al. Breaking the coherence barrier: A new theory for compressed sensing. arXiv preprint arXiv: 1302.0561, 2013.
[23]
Adcock B, Hansen AC, Poon C. Beyond consistent reconstructions: optimality and sharp bounds for generalized sampling, and application to the uniform resampling problem. SIAM Journal on Mathematical Analysis, 2013, 45(5): 3132-3167.
[24]
Adcock B, Hansen AC, Shadrin A. A stability barrier for reconstructions from Fourier samples. SIAM Journal on Numerical Analysis, 2014, 52(1): 125-139.
[25]
Donoho DL, Elad M. Optimally sparse representation in general (nonorthogonal) dictionaries via L1 minimization. Proceedings of the National Academy of Sciences, 2003, 100(5): 2197-2202.

PREV Smooth fitting of bias field in prostate MRI with peak detection
NEXT Applications of blood oxygen level dependent-functional magnetic resonance imaging in patients with moyamoya disease
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn