MR image reconstruction via sparse representation: Modeling and algorithm

Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009

Volumn 1, Issue , 2009, Pages 10-16

  Ye, Xiaojing ^a   Chen, Yunmei ^a   Huang, Feng ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b Invivo Corporation   (United States)

Author keywords

Compressed sensing; Convex optimization; Dictionary; MRI reconstruction; Sparse representation

Indexed keywords

ACQUISITION TIME; COMPRESSIVE SENSING; COMPUTATIONAL COSTS; DATA FIDELITY; EFFICIENT NUMERICAL SCHEMES; FIDELITY TERM; FINE STRUCTURES; HOT RESEARCH TOPICS; IN-VIVO; K-SPACE; MODEL PARAMETERS; MR IMAGES; MRI RECONSTRUCTION; PARAMETER DECISION; PARAMETER SELECTION; POTENTIAL LOSS; PRIOR KNOWLEDGE; SPARSE REPRESENTATION; TOTAL VARIATION;

COMPUTER VISION; CONVEX OPTIMIZATION; GLOSSARIES; IMAGE RECONSTRUCTION;

MAGNETIC RESONANCE;

EID: 84856734581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. E. J. Candes, J. K. Romberg, and T. Tao, "Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, 2006.
2. M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging," Magn. Reson. Med., vol. 58, no. 6, pp. 1182-1195, 2007.
3. T. Chang, L. He, and T. Fang, "MR Image Reconstruction from Sparse Radial Samples Using Bregman Iteration," Proceedingss of Anaal Meeting of ISMRM, p. 696, 2006.
4. J. Ye, S. Tak, Y. Han. and H. Park, "Projection Reconstruction MR Imaging Using FOCUSS," Magn. Reson. Med, vol. 57, pp. 764-775, 2007.
5. K. Block, M. Uecker, and J. Frahm, "Undersampled Radial MRI with Multiple Coils. Iterative Image Reconstruction Using a Total Variation Consraint," Magn. Reson. Med., vol. 57, pp. 1086-1098, 2007.
6. E. J. Candes and J. K. Romberg, "Signal Recovery from Random Projections," Proceedings of SPIE Computational Imaging III, vol. 5674, pp. 76-86, 2005.
7. S. Chen, D. Donoho, and M. Saunders, "Atomic Decomposition by Basis Pursuit," SIAM J. Sci. Comput., vol. 20, pp. 33-61, 1999.
8. S. Kim, K. Koh, M. Lustig, and S. Boyd, "An Efficient Method for Compressed Sensing," Proceedings of IEEE Intl. Conf. Image Process. (lClP), vol. 3, pp. 117-120, 2007.
9. D. L. Donoho, M. Elad, and V. Temlyakov, "Stable Recovery of Sparse Overcomplete Represenation in the Presence of Noise," IEEE Trans. Inf. Theory, vol. 52, pp. 6-18, 2006.
10. H. Liao and G. Sapiro, "Sparse Representation for Limited Data Tomography." Proceedings of IEEE Intl. Symp. Biomed Imag. (ISBI), pp. 1375-1378, 2008.
11. T. Goldstein and S. Osher, "The Split Bregman Method for LI Regularized Problems, Tech. Rep. CAM Report 08-29, UCLA.
12. L. Rudin, S. Osher, and E. Fatemi, "Non-linear Total Variation Noise Removal Algorithm," Physics D., vol. 60, pp. 259-268, 1992.
13. J. Starck, M. Elad, and D. Donoho, "Image Decomposition via the Combination of Sparse Representations and a Variational Approach," IEEE Trans. Image Process., vol. 14, pp. 1570-1582, 2005.
14. J. Yang, Y. Zhang, and W. Yin, "A Fast TVL1-L2 Minimization Algorithm for Signal Reconstruction from Partial Fourier Data," CAAM, Rice Univ., Tech. Rep. TR08-29, 2008.
15. D. L. Donoho, "For Most Large Underdetennined Systems of Linear Equations, the Minimal 11-norm Solution is Also the Sparsest Solution," Commun. Pure Appl. Math., vol. 59, no. 7, pp. 907-934. 2006.
16. L. He, T.-C. Chang, S. Osher, T. Fang, and P. Speier, "MR Image Reconstruction by Using the Iterative Refinement Method and Nonlinear Inverse Scale Space Methods," CAM Report 06-35, UCLA, 2006.
17. K. Kreutz-Delgado. J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, "Dictionary Learning Algorithms for Sparse Representation," Neural Comp., vol. 15, pp. 349-396, 2003.
18. J. F. Murray and K. Kreutz-Delgado, "An Improved Focuss-based Learning Algorithm for Solving Sparse Linear Inverse Problems," IEEE Int. Conf. Signals. Syst. Comput., pp. 347-351, 2001.
19. M. Aharon, M. Elad, and A. Bruckstein, "The K-SVD: An algorithm for Designing of Overcomplete Dictionaries for Sparse Representation," IEEE Trans. Signal Process, vol. 54, no. 11, pp. 4311-4322, 2006.
20. M. Elad and M. Aharon, "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries," IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, 2006.
21. 1-Minimization with Applications to Compressed Sensing," SIAM J. Imaging Science, vol. 1, no. 1, pp. 143-168, 2008.
22. S. Osher, Y. Mao, B. Dong, and W. Yin, "Fast Linearized Bregman Iteration for Compressive Sensing and Sparse Denoising, Tech. Rep. CAM Report 08-37, UCLA.
23. J.-F. Cai, S. Osher, and Z. Shen, "Linearized Bregman Iterations for Compressed Sensing," CAM Report 08-06, UCLA, 2008.
24. Y. Wang, J. Yang, W. Yin, and Y. Zhang, "A New Alternating Minimization Algorithm for Total Variation Image Reconstruction," SIAM J. Imaging Science, vol. 1, no. 3, pp. 248-272, 2008.
25. R. Courant, "Variational Methods for the Solution of Problems with Equilibrium and Vibration," Bull. Am. Math. Soc, vol. 49, pp. 1-23, 1943.