Accelerated dynamic MRI exploiting sparsity and low-rank structure: K-t SLR

IEEE Transactions on Medical Imaging

Volumn 30, Issue 5, 2011, Pages 1042-1054

  Lingala, Sajan Goud ^a   Hu, Yue ^a   Dibella, Edward ^b   Jacob, Mathews ^c  

^a University of Rochester   (United States)

^b UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF ROCHESTER   (United States)

Author keywords

Data driven transforms; dynamic magnetic resonance imaging (MRI); k t SLR; low rank and sparse matrix recovery

Indexed keywords

BANDED STRUCTURES; BASIS FUNCTIONS; CINE-MRI; CLASSICAL MODEL; COMPACT REPRESENTATION; DATA DRIVEN; DATA SETS; DYNAMIC IMAGING; DYNAMIC MAGNETIC RESONANCE IMAGING; DYNAMIC MAGNETIC RESONANCE IMAGING (MRI); DYNAMIC MRI; EXISTING METHOD; FOURIER SPACE; HIGH QUALITY RECONSTRUCTION; IN-VIVO; K L TRANSFORM; K-T SLR; LOW RANK; MATRIX RECOVERY; MEASURED DATA; NOVEL ALGORITHM; NUMERICAL PHANTOMS; PERFUSION MRI; RANK STRUCTURE; RECOVERY RATE; SPACE DATA; TEMPORAL BASIS FUNCTIONS; TWO-STEP APPROACH;

ALGORITHMS; MAGNETIC DOMAINS; MATRIX ALGEBRA; MEDICAL IMAGING; METADATA; NUMERICAL METHODS; PRINCIPAL COMPONENT ANALYSIS; RECOVERY; RESONANCE;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BREATHING MECHANICS; COMPUTER SIMULATION; HEART; HISTOLOGY; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REPRODUCIBILITY; SIGNAL PROCESSING;

ALGORITHMS; COMPUTER SIMULATION; HEART; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING, CINE; MODELS, CARDIOVASCULAR; PHANTOMS, IMAGING; REPRODUCIBILITY OF RESULTS; RESPIRATORY MECHANICS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 79955624809     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2010.2100850     Document Type: Article

References (45)

1. Z. Liang, H. Jiang, C. Hess, and P. Lauterbur, "Dynamic imaging by model estimation," Int. J. Imag. Syst. Technol., vol. 8, no. 6, pp. 551-557, 1997. (Pubitemid 127549493)
2. B. Madore, "Using UNFOLD to remove artifacts in parallel imaging and in partial-Fourier imaging," Magn. Reson. Med., vol. 48, no. 3, pp. 493-501, Sep. 2002. (Pubitemid 34951327)
3. J. Tsao, P. Boesiger, and K. Pruessmann, "kt BLAST and kt SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations," Magn. Reson. Med., vol. 50, no. 5, pp. 1031-1042, 2003. (Pubitemid 37356583)
4. B. Sharif and Y. Bresler, "Adaptive real-time cardiac MRI using PARADISE: Validation by the physiologically improved NCAT phantom," in Proc. ISBI, 2007.
5. B. Sharif, J. A. Derbyshire, A. Z. Faranesh, and Y. Bresler, "Patient-adaptive reconstruction and acquisition in dynamic imaging with sensitivity encoding (PARADISE),"Magn. Reson. Med., pp. 501-513, 2010.
6. M.Lustig,J.Santos, D. Donoho, andJ.Pauly, "kt SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity," in Proc. 13th Annu. Meeting ISMRM, Seattle, WA, 2006, p. 2420.
7. G. Adluru, S. Awate, T. Tasdizen, R. Whitaker, and E. DiBella, "Temporally constrained reconstruction of dynamic cardiac perfusion MRI," Magn. Reson. Med., vol. 57, no. 6, pp. 1027-1036, 2007. (Pubitemid 46911287)
8. Z. Liang, "Spatiotemporal imaging with partially separable functions," in Proc. ISBI, 2007, pp. 181-182.
9. C. Brinegar, Y. Wu, L. Foley, T. Hitchens, Q. Ye, C. Ho, and Z. Liang, "Real-time cardiac MRI without triggering, gating, or breath holding," in Int. Conf. IEEE Eng. Med. Biol. Soc., 2008, pp. 3381-3384.
10. C. Brinegar, H. Zhang, Y.Wu, L.Foley, T. Hitchens, Q. Ye, D. Pocci, F. Lam, C. Ho, and Z. Liang, "Real-time cardiac MRI using prior spatial-spectral information," in Int. Conf. IEEE Eng. Med. Biol. Soc., 2009, vol. 1, p. 4383.
11. H. Pedersen, S. Kozerke, S. Ringgaard, K. Nehrke, and W. Y. Kim, "k-t PCA: Temporally constrained k-t BLAST reconstruction using principal component analysis," Magn. Reson. Med., vol. 62, no. 3, pp. 706-716, Sep. 2009.
12. H. Jung, J. Park, J. Yoo, and J. C. Ye, "Radial k-t FOCUSS for high-resolution cardiac cine MRI," Magn. Reson. Med., vol. 63, no. 1, p. 68, Oct. 2009.
13. E. Candes and B. Recht, "Exact matrix completion via convex optimization," Foundations Computat. Math., vol. 9, no. 6, pp. 717-772, 2009.
14. J. Cai, E. Candes, and Z. Shen, "A singular value thresholding algorithm for matrix completion," 2008.
15. K. Lee and Y. Bresler, "Admira: Atomic decomposition for minimum rank approximation," 2009.
16. K. Lee and Y. Bresler, "Guaranteed minimum rank approximation from linear observations by nuclear norm minimization with an ellipsoidal constraint," 2009.
17. R. Chatrand, "Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data," in Proc. IEEE ISBI, 2009, pp. 262-265.
18. 0 -minimization," IEEE Trans. Med. Imag., vol. 28, no. 1, pp. 106-121, Jan. 2009.
19. M. Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables," J. R. Stat. Soc: Series B (Stat. Methodol), vol. 68, no. 1, pp. 49-67, 2006. (Pubitemid 43415335)
20. S. Goud, Y. Hu, and M. Jacob, "Real-time cardiac MRI using low-rank and sparsity penalties," in Proc. ISBI, 2010, pp. 988-991.
21. J. Haldar and Z.-P. Liang, "Spatiotemporal imaging with partially separable functions: A matrix recovery approach," in Proc. ISBI, 2010, pp. 716-719.
22. K. Toh and S. Yun, An accelerated proximal gradient method for nuclear norm regularized least squares Dept. Math, Nat. Univ. Singapore, 2009.
23. S. Ma, D. Goldfarb, and L. Chen, "Fixed point and Bregman iterative methods for matrix rank minimization," 2009.
24. Y. Wang, J. Yang, W. Yin, and Y. Zhang, "A new alternating minimization algorithm for total variation image reconstruction," SIAM J. Imag. Sci., vol. 1, no. 3, pp. 248-272, 2008.
25. J. Yang, Y. Zhang, and W. Yin, "A fast TVL1-L2 minimization algorithm for signal reconstruction from partial Fourier data," IEEE J. Special Topics Signal Process., vol. 4, no. 2, pp. 288-297, 2008.
26. B. Recht, M. Fazel, and P. Parrilo, "Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization," SIAM Rev., vol. 52, pp. 471-501, 2010.
27. J. Trzasko and A. Manduca, "Relaxed conditions for sparse signal recovery with general concave priors," IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4347-4354, Nov. 2009.
28. D. Wipf and S. Nagarajan, "Iterative reweighted l1 and l2 methods for finding sparse solutions," presented at the SPARS09, Rennes, France, 2009.
29. I. Gorodnitsky and B. Rao, "Sparse signal reconstruction from limited data using focuss: A re-weighted minimum norm algorithm," IEEE Trans. Signal Process., vol. 45, no. 3, pp. 600-616, Mar. 1997. (Pubitemid 127765947)
30. E. Candes, M. Wakin, and S. Boyd, "Enhancing sparsity by reweighted 1 minimization," J. Fourier Anal. Appl., vol. 14, no. 5, pp. 877-905, 2008.
31. R. Chartrand, "Exact reconstruction of sparse signals via nonconvex minimization," IEEE Signal Process. Lett., vol. 14, no. 10, pp. 707-710, Oct. 2007. (Pubitemid 47434090)
32. A. Majumdar and R. Ward, "An algorithm for sparse MRI reconstruction by schatten p-norm minimization," Magn. Reson. Imag., to be published.
33. A. Chambolle and P. Lions, "Image recovery via total variation minimization and related problems," Numerische Mathematik, vol. 76, no. 2, pp. 167-188, 1997.
34. J. Yang and Y. Zhang, "Alternating direction algorithms for l1-prob-lems in compressive sensing," 2009.
35. S. Wright and J. Nocedal, Numerical Optimization. New York: Springer, 2006.
36. Accelereyes Jacket. Accelereyes, 2010 [Online]. Available: http://www.accelereyes.com
37. W. Segars and B. Tsui, "Study of the efficacy of respiratory gating in myocardial spect using the new 4-d NCAT phantom," IEEE Trans. Nucl. Sci., vol. 49, no. 3, p. 675, Jun. 2002.
38. B. Sharif and Y. Bresler, "Adaptive real-time cardiac MRI using PARADISE: Validation by the physiologically improved ncat phantom," in Proc. 4th IEEE Int. Symp. Biomed. Imag., 2007, pp. 1020-1023. (Pubitemid 350141265)
39. F.-H. Lin, B1 simulator: Simulate b1 field for MRI RF coils [Online]. Available: http://www.nmr.mgh.harvard.edu/~fhlin/
40. M. Jacob, "Optimized least-square nonuniform fast Fourier transform," IEEE Trans. Signal Process., vol. 57, no. 6, pp. 2165-2177, Jun. 2009.
41. S. Ramani, T. Blu, and M. Unser, "Monte-carlo sure: A black-box optimization of regularization parameters for general denoising algorithms," IEEE Trans. Image Process., vol. 17, no. 9, pp. 1540-1554, Sep. 2008.
42. G. Adluru, C. McGann, P. Speier, E. Kholmovski, A. Shaaban, and E. DiBella, "Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging," J. Magn. Reson. Imag., vol. 29, no. 2, pp. 466-473, 2009.
43. M. Afonso, J. Bioucas-Dias, and M. Figueiredo, "An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems," IEEE Trans. Image Process., vol. 20, no. 3, pp. 681-695, 2011.
44. M. A. Guttman, P. Kellman, A. J. Dick, R. J. Lederman, and E. R. McVeigh, "Real-time accelerated interactive MRI with adaptive TSENSE and UNFOLD," Magn. Reson. Med., vol. 50, no. 2, pp. 315-21, Aug. 2003. (Pubitemid 36950851)
45. P. Kellman, F. H. Epstein, and E. R. McVeigh, "Adaptive sensitivity encoding incorporating temporal filtering (tsense)," Magn. Reson. Med., vol. 45, no. 5, pp. 846-852, May 2001. (Pubitemid 32402027)