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Compressed Sensing Based Dynamic MR Imaging: A Short Survey  

Jung, Hong (KAIST)
Ye, Jong-Chul (KAIST)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.5, 2009 , pp. 25-31 More about this Journal
Abstract
The recently developed sampling theory, "compressed sensing" is gathering huge interest in MR reconstruction area because of its feasibility of high spatio-temporal resolution of dynamic MRI which has been limited in conventional methods based on Nyquist sampling theory. Since dynamic MRI usually has high redundant information along temporal direction, this can be very sparsely represented in most of cases. Therefore, compressed sensing that exploits the sparsity of unknown images can be effectively applied in most of dynamic MRI. This review article briefly introduces currently proposed compressed sensing based dynamic MR imaging algorithms and other methods exploiting sparsity. By comparing them with conventional methods, you may have insight how the compressed sensing based methods can impact nearly every area of clinical dynamic MRI.
Keywords
compressed sensing; dynamic MRI; sparsity; k-t FOCUSS; k-t SPARSE;
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1 Lustig M, Santos JM, Donoho DL, Pauly JM. "k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity," In Proceedings of ISMRM, Seattle, WA, April 2006
2 Xu D, King KF, Liang Z-P. "Improving k-t SENSE by Adaptive Regularization," Magn Reson Med, vol. 57, pp. 918–930, 2007   DOI   ScienceOn
3 H. Jung, K. H. Sung, K. S. Nayak, E. Y. Kim, and J. C. Ye, "k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI," Magn Reson Med, vol. 61, pp. 103-116, January 2009   DOI   ScienceOn
4 Lustig M, Donoho DL, Pauly JM. "Sparse MRI: The application of compressed sensing for rapid MR imaging," Magn Reson Med, vol. 58, pp. 1182–1195, 2007   DOI   ScienceOn
5 Qiu C, Lu W, Vaswani N, "Real-time dynamic MR image reconstruction using Kalman filtered compressed sensing," IEEE Intl. Conf. Acoustics, Speech, Sig. Proc.(ICASSP), 2009
6 Jeffrey Tsao, Boesiger P, Pruessmann KP. "k-t BLAST and k-t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations," Magn Reson Med, vol. 50, pp. 1031–.1042, 2003   DOI   ScienceOn
7 Candes E, Romberg J, Tao T. "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans Info Theory, vol. 52, pp. 489–.509, 2006   DOI   ScienceOn
8 Z. Liang, "Spatiotemporal Imaing with Partially Separable Functions," in 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007, pp. 988-991, 2007
9 Donoho DL. "Compressed sensing," IEEE Trans on Inform Theory, vol. 52, pp. 1289–.1306, 2006   DOI   ScienceOn
10 Mistretta CA, Wieben O, Velikina J, Block WF, Perry J, Wu Y, Johnson K, Wu Y. "Highly constrained backprojection for time-resolved MRI," Magn Reson Med, vol. 55, pp. 30–40, 2006   DOI   ScienceOn
11 O'Halloran R, Wen Z, Holmes JH, Fain SB, "Iterative projection reconstruction of time- resolved images using highly-constrained back- projection (HYPR)," Magn. Reson. Med. vol. 59, pp. 132-139, 2008   DOI   ScienceOn
12 H. Jung, J. C. Ye, and E. Y. Kim, "Improved k-t BLAST and k-t SENSE using FOCUSS," Physics in Medicine and Biology, vol. 52, pp. 3201-3226, June 2007   DOI   ScienceOn