Dictionary learning and time sparsity in dynamic MRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7510 LNCS, Issue , 2012, Pages 256-263

  Caballero, Jose ^a   Rueckert, Daniel ^a   Hajnal, Joseph V ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b HAMMERSMITH HOSPITAL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MEDICAL COMPUTING; MEDICAL IMAGING;

DICTIONARY LEARNING; LEARNED DICTIONARIES; POTENTIAL BENEFITS; SPARSE RECONSTRUCTION; SPARSE REPRESENTATION; SPARSITY CONSTRAINTS; SPEED LIMIT; TEMPORAL GRADIENTS;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; SIGNAL PROCESSING; STATISTICAL MODEL; THEORETICAL MODEL; TIME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; PHANTOMS, IMAGING; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS;

MLCS; MLOWN;

EID: 84872529331     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33415-3_32     Document Type: Conference Paper

References (17)

1. Candes, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489–509 (2006)
2. Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
3. Tsao, J., Boesiger, P., Pruessmann, K.P.: k-t BLAST and k-t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magnetic Resonance in Medicine 50(5), 1031–1042 (2003)
4. Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine 58(6), 1182–1195 (2007)
5. Jung, H., Ye, J.C., Kim, E.Y.: Improved k-t BLAST and k-t SENSE using FO-CUSS. Physics in Medicine and Biology 52(11), 3201–3226 (2007)
6. Jung, H., Sung, K., Nayak, K.S., Kim, E.Y., Ye, J.C.: k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI. Magnetic Resonance in Medicine 61(1), 103–116 (2009)
7. Gamper, U., Boesiger, P., Kozerke, S.: Compressed sensing in dynamic MRI. Magnetic Resonance in Medicine 59(2), 365–373 (2008)
8. Usman, M., Prieto, C., Schaeffter, T., Batchelor, P.G.: k-t group sparse: A method for accelerating dynamic MRI. Magnetic Resonance in Medicine 66(4), 1163–1176 (2011)
9. Lustig, M., Santos, J.M., Donoho, D.L., Pauly, J.M.: k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity. In: Proc. of the 13th Annual Meeting of ISMRM, Seattle, vol. 50(5), p. 2420 (2006)
10. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: Design of dictionaries for sparse representation. In: Proc. of SPARS, vol. 5, pp. 9–12 (2005)
11. Ravishankar, S., Bresler, Y.: MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE TMI 30(5), 1028–1041 (2011)
12. Chen, Y., Ye, X., Huang, F.: A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data. Inverse Problems and Imaging 4(2), 223–240 (2010)
13. Tropp, J.A., Wright, S.J.: Computational methods for sparse solution of linear inverse problems. Proc. of the IEEE 98(6), 948–958 (2010)
14. Protter, M., Elad, M.: Image sequence denoising via sparse and redundant representations. IEEE Transactions on Image Processing 18(1), 27–35 (2009)
15. Montefusco, L.B., Lazzaro, D., Papi, S., Guerrini, C.: A fast compressed sensing approach to 3D MR image reconstruction. IEEE TMI 30(5), 1067–1075 (2011)
16. Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: Proc. of 27th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 40–44 (2011)
17. Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1(4), 606–617 (2007)