Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 546814, 12 pages
http://dx.doi.org/10.1155/2014/546814
Research Article

Energy Preserved Sampling for Compressed Sensing MRI

1School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
2Brain Imaging Laboratory, Department of Psychiatry, Columbia University, New York, NY 10032, USA
3MRI Unit, New York State Psychiatric Institute, New York, NY 10032, USA

Received 1 November 2013; Revised 3 March 2014; Accepted 6 March 2014; Published 26 May 2014

Academic Editor: William Crum

Copyright © 2014 Yudong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Macovski, “MRI: a charmed past and an exciting future,” Journal of Magnetic Resonance Imaging, vol. 30, no. 5, pp. 919–923, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. G. A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, vol. 14, no. 1, pp. 56–66, 1997. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Scheffler, “A pictorial description of steady-states in rapid magnetic resonance imaging,” Concepts in Magnetic Resonance, vol. 11, no. 5, pp. 291–304, 1999. View at Google Scholar · View at Scopus
  4. P. Mansfield, “Multi-planar image formation using NMR spin echoes,” Journal of Physics C: Solid State Physics, vol. 10, no. 3, article 004, pp. L55–L58, 1977. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Hutchinson and U. Raft, “Fast MRI data acquisition using multiple detectors,” Magnetic Resonance in Medicine, vol. 6, no. 1, pp. 87–91, 1988. View at Google Scholar · View at Scopus
  6. D. Kwiat, S. Einav, and G. Navon, “A decoupled coil detector array for fast image acquisition in magnetic resonance imaging,” Medical Physics, vol. 18, no. 2, pp. 251–265, 1991. View at Publisher · View at Google Scholar · View at Scopus
  7. D. K. Sodickson, C. A. McKenzie, M. A. Ohliger, E. N. Yeh, and M. D. Price, “Recent advances in image reconstruction, coil sensitivity calibration, and coil array design for SMASH and generalized parallel MRI,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 13, no. 3, pp. 158–163, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Yang, Y. Zhang, and W. Yin, “A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data,” IEEE Journal on Selected Topics in Signal Processing, vol. 4, no. 2, pp. 288–297, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: the application of compressed sensing for rapid MR imaging,” Magnetic Resonance in Medicine, vol. 58, no. 6, pp. 1182–1195, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Seeger, H. Nickisch, R. Pohmann, and B. Schölkopf, “Optimization of k-space trajectories for compressed sensing by Bayesian experimental design,” Magnetic Resonance in Medicine, vol. 63, no. 1, pp. 116–126, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Knoll, C. Clason, C. Diwoky, and R. Stollberger, “Adapted random sampling patterns for accelerated MRI,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 24, no. 1, pp. 43–50, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Nasehi Tehrani, C. Jin, A. McEwan, and A. van Schaik, “A comparison between compressed sensing algorithms in electrical impedance tomography,” in Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '10), vol. 2010, pp. 3109–3112, 2010. View at Publisher · View at Google Scholar
  13. D. Donoho and J. Tanner, “Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1906, pp. 4273–4293, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing, vol. 20, no. 1, pp. 33–61, 1998. View at Google Scholar · View at Scopus
  15. E. J. Candes and J. Romberg, “Practical signal recovery from random projections,” in Computational Imaging III, Proceedings of SPIE, pp. 76–86, San Jose, Calif, USA, January 2005. View at Publisher · View at Google Scholar
  16. D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 45, pp. 18914–18919, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Communications on Pure and Applied Mathematics, vol. 57, no. 11, pp. 1413–1457, 2004. View at Publisher · View at Google Scholar · View at Scopus
  18. D. L. Donoho, M. Elad, and V. N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Transactions on Information Theory, vol. 52, no. 1, pp. 6–18, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Van Den Berg and M. P. Friedlander, “Sparse optimization with least-squares constraints,” SIAM Journal on Optimization, vol. 21, no. 4, pp. 1201–1229, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Becker, J. Bobin, and E. J. Candès, “NESTA: a fast and accurate first-order method for sparse recovery,” SIAM Journal on Imaging Sciences, vol. 4, no. 1, pp. 1–39, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, 2009. View at Publisher · View at Google Scholar
  22. I. Bayram and I. W. Selesnick, “A subband adaptive iterative shrinkage/thresholding algorithm,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1131–1143, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo, “A fast algorithm for the constrained formulation of compressive image reconstruction and other linear inverse problems,” in Proceedings of the 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, pp. 4034–4037, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Guerquin-Kern, M. Haberlin, K. P. Pruessmann, and M. Unser, “A fast wavelet-based reconstruction method for magnetic resonance imaging,” IEEE Transactions on Medical Imaging, vol. 30, no. 9, pp. 1649–1660, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI: a look at how CS can improve on current imaging techniques,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 72–82, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. Y.-C. Kim, S. S. Narayanan, and K. S. Nayak, “Accelerated three-dimensional upper airway MRI using compressed sensing,” Magnetic Resonance in Medicine, vol. 61, no. 6, pp. 1434–1440, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Ma, J. B. Son, J. A. Bankson, R. J. Stafford, H. Choi, and D. Ragan, “A fast spin echo two-point Dixon technique and its combination with sensitivity encoding for efficient T2-weighted imaging,” Magnetic Resonance Imaging, vol. 23, no. 10, pp. 977–982, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Zhang, B. Peterson, and Z. Dong, “A support-based reconstruction for SENSE MRI,” Sensors, vol. 13, no. 4, pp. 4029–4040, 2013. View at Publisher · View at Google Scholar
  29. Y. Zhang, L. Wu, B. Peterson, and Z. Dong, “A two-level iterative reconstruction method for compressed sensing MRI,” Journal of Electromagnetic Waves and Applications, vol. 25, no. 8-9, pp. 1081–1091, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Studholme, V. Cardenas, E. Song, F. Ezekiel, A. Maudsley, and M. Weiner, “Accurate template-based correction of brain MRI intensity distortion with application to dementia and aging,” IEEE Transactions on Medical Imaging, vol. 23, no. 1, pp. 99–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. N. A. Okaeme and P. Zanchetta, “Hybrid bacterial foraging optimization strategy for automated experimental control design in electrical drives,” IEEE Transactions on Industrial Informatics, vol. 9, no. 2, pp. 668–678, 2013. View at Publisher · View at Google Scholar
  32. E. Sharon, N. Presman, and S. Litsyn, “Convergence analysis of generalized serial message-passing schedules,” IEEE Journal on Selected Areas in Communications, vol. 27, no. 6, pp. 1013–1024, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. I. Bergel and A. Leshem, “Convergence analysis of downstream VDSL adaptive multichannel partial FEXT cancellation,” IEEE Transactions on Communications, vol. 58, no. 10, pp. 3021–3027, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. Y. Zhang and L. Wu, “An mr brain images classifier via principal component analysis and kernel support vector machine,” Progress in Electromagnetics Research, vol. 130, pp. 369–388, 2012. View at Publisher · View at Google Scholar
  35. Y. Zhang, S. Wang, G. Ji, and Z. Dong, “An MR brain images classifier system via particle swarm optimization and kernel support vector machine,” The Scientific World Journal, vol. 2013, Article ID 130134, 9 pages, 2013. View at Publisher · View at Google Scholar
  36. C. Chen and J. Huang, “The benefit of tree sparsity in accelerated MRI,” Medical Image Analysis, 2013. View at Publisher · View at Google Scholar
  37. C. Chen and J. Huang, “Compressive sensing MRI with wavelet tree sparsity,” in Proceedings of the 26th Annual Conference on Advances in Neural Information Processing Systems (NIPS '12), Advances in Neural Information Processing Systems 25, pp. 1124–1132, Lake Tahoe, Nev, United States, December 2012.
  38. L. He and L. Carin, “Exploiting structure in wavelet-based bayesian compressive sensing,” IEEE Transactions on Signal Processing, vol. 57, no. 9, pp. 3488–3497, 2009. View at Publisher · View at Google Scholar · View at Scopus