Dictionary learning and time sparsity for dynamic MR data reconstruction

IEEE Transactions on Medical Imaging

Volumn 33, Issue 4, 2014, Pages 979-994

  Caballero, Jose ^a   Price, Anthony N ^b   Rueckert, Daniel ^a   Hajnal, Joseph V ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b ST THOMAS HOSPITAL   (United Kingdom)

Author keywords

Compressed sensing (CS); Dictionary learning; Dynamic magnetic resonance imaging; Image reconstruction; Sparse coding

Indexed keywords

ALGORITHMS; MAGNETIC RESONANCE IMAGING;

ACQUISITION PROCESS; COMPRESSIVE SENSING; DATA RECONSTRUCTION; DICTIONARY LEARNING; DYNAMIC MAGNETIC RESONANCE IMAGING; ITERATIVE ALGORITHM; REAL AND IMAGINARY; SPARSE CODING;

IMAGE RECONSTRUCTION;

ALGORITHM; ARTICLE; BOOK; DICTIONARY LEARNING WITH TEMPORAL GRADIENT; DYNAMIC MAGNETIC RESONANCE IMAGING; HUMAN; IMAGE RECONSTRUCTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PERFORMANCE MEASUREMENT SYSTEM; QUALITY CONTROL; RETROSPECTIVE STUDY; SPATIOTEMPORAL ANALYSIS; THREE DIMENSIONAL IMAGING; TIME; ANATOMY AND HISTOLOGY; HEART; IMAGE PROCESSING; PROCEDURES; WAVELET ANALYSIS;

ALGORITHMS; HEART; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING, CINE; WAVELET ANALYSIS;

EID: 84898005304     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2014.2301271     Document Type: Article

References (49)

1. C. E. Shannon, "Communication in the presence of noise," in Proc. Int. Radio Eng., Jan. 1949, vol. 37, pp. 10-21, 1.
2. B. Madore, G. H. Glover, and N. J. Pelc, "Unaliasing by Fourier-encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI," Magn. Reson. Med., vol. 42, no. 5, pp. 813-828, 1999. (Pubitemid 29521471)
3. M. E. Brummer, D. Moratal-Pérez, C.-Y. Hong, R. I. Pettigrew, J. Millet-Roig, and W. T. Dixon, "Noquist: Reduced field-of-view imaging by direct Fourier inversion," Magn. Reson. Med., vol. 51, no. 2, pp. 331-342, 2004. (Pubitemid 38168593)
4. Q. Zhao, N. Aggarwal, and Y. Bresler, "Dynamic imaging of time-varying objects," in Proc. 9th Annu. Meet. ISMRM, Glasgow, UK, 2001, p. 1776.
5. N. Aggarwal, Q. Zhao, and Y. Bresler, "Spatio-temporal modeling and minimum redundancy adaptive acquisition in dynamic MRI," in Proc. 1st IEEE Int. Symp. Biomed. Imag., Washington, DC, Jul. 2002, pp. 737-740.
6. Y. Bresler, "Fast acquisition and sampling in MRI: Introduction to time-sequential sampling of spatio-temporal signals," in Proc. 1st IEEE Int. Symp. Biomed. Imag., Washington, DC, Jul. 2002, pp. 713-716.
7. N. Aggarwal, S. Bandyopadhyay, and Y. Bresler, "Spatio-temporal modeling and adaptive acquisition for cardiacMRI," in Proc. 2nd IEEE Int. Symp. Biomed. Imag., Washington, DC, Apr. 2004, pp. 628-631. (Pubitemid 40514015)
8. N. Aggarwal, B. Sutton, and Y. Bresler, "Adaptive dynamic imaging in cardiac MR fluoroscopy: First in-vivo results," in Proc. 14th Annu. Meeting ISMRM, Seattle, WA, May 2006, p. 205.
9. N. Aggarwal and Y. Bresler, "Patient-adapted reconstruction and acquisition dynamic imaging method (PARADIGM) for MRI," Inverse Problems, vol. 24, no. 4, p. 045015 (29pp), 2008.
10. B. Sharif, J. A. Derbyshire, A. Z. Faranesh, and Y. Bresler, "Patientadaptive reconstruction and acquisition in dynamic imaging with sensitivity encoding (PARADISE)," Magn. Reson. Med., vol. 64, no. 2, pp. 501-513, Aug. 2010.
11. J. Tsao, P. Boesiger, and K. Pruessmann, " - BLAST and - SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations," Magn. Reson. Med., vol. 50, no. 5, pp. 1031-1042, 2003. (Pubitemid 37356583)
12. S. J. Malik, S. Schmitz, D. O'Regan, D. J. Larkman, and J. V. Hajnal, " - Choice: Reconstruction of undersampled dynamic MRI by data-driven alias rejection applied to contrast-enhanced angiography," Magn. Reson. Med., vol. 56, no. 4, pp. 811-823, Oct. 2006. (Pubitemid 44477664)
13. D. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
14. E. Candes, J. 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, Feb. 2006. (Pubitemid 43174093)
15. 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, Dec. 2007. (Pubitemid 350234205)
16. H. Jung, J. C. Ye, and E. Y. Kim, "Improved - BLAST and - SENSE using FOCUSS," Phys. Med. Biol., vol. 52, no. 11, pp. 3201-3226, Jun. 2007. (Pubitemid 46795256)
17. M. Lustig, J. M. Santos, D. L. Donoho, and J. M. Pauly, " - SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity," in Proc. 14th Annu. Meet. ISMRM, Seattle, WA, 2006, p. 2420.
18. L. B. Montefusco, D. Lazzaro, S. Papi, and C. Guerrini, "A fast compressed sensing approach to 3D MR image reconstruction," IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1064-1075, May 2011.
19. 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.
20. M. Usman, C. Prieto, T. Schaeffter, and P. G. Batchelor, " - Group sparse: A method for accelerating dynamicMRI," Magn. Reson. Med., vol. 66, no. 4, pp. 1163-1176, Oct. 2011.
21. I. Tosic and P. Frossard, "Dictionary learning," IEEE Signal Process. Mag., vol. 28, no. 2, pp. 27-38, 2011.
22. 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, Dec. 2006. (Pubitemid 44811686)
23. J. Mairal, G. Sapiro, and M. Elad, "Learning multiscale sparse representations for image and video restoration," Multiscale Model. Simulat., vol. 7, no. 1, pp. 214-241, 2008.
24. I. Ramírez, P. Sprechmann, and G. Sapiro, "Classification and clustering via dictionary learning with structured incoherence and shared features," in Proc. 23rd IEEE Conf. CVPR, San Francisco, CA, Jun. 2010, pp. 3501-3508.
25. S. Ravishankar and Y. Bresler, "MR image reconstruction from highly undersampled -space data by dictionary learning," IEEE Trans. Med. Imag., vol. 30, no. 5, pp. 1028-1041, May 2011.
26. J. Caballero, D. Rueckert, and J. V. Hajnal, "Dictionary learning and time sparsity in dynamicMRI," inProc. 15th Int. Conf. MICCAI, Nice, France, Oct. 2012, vol. 1, pp. 256-263.
27. S. P. Awate and E. V. R. Di Bella, "Spatiotemporal dictionary learning for undersampled dynamic MRI reconstruction via joint frame-based and dictionary-based sparsity," in Proc. 9th IEEE Int. Symp. Biomed. Imag., Barcelona, Spain, 2012, pp. 318-321.
28. M. Doneva, P. Börnert, H. Eggers, C. Stehning, J. Sénégas, and A. Mertins, "Compressed sensing reconstruction for magnetic resonance parameter mapping," Magn. Reson. Med., vol. 64, no. 4, pp. 1114-1120, 2010.
29. E. J. Candès and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. (Pubitemid 41800353)
30. E. J. Candès, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Commun. Pure Ap. Math., vol. 59, no. 8, pp. 1207-1223, 2006. (Pubitemid 43988295)
31. J. A. Tropp, Anna, and C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
32. Y. Wang, L. Ying, and Y. Zhou, "Undersampled dynamic magnetic resonance imaging using patch-based spatiotemporal dictionaries," in Proc. 10th IEEE Int. Symp. Biomed. Imag., San Francisco, CA, Apr. 2013, pp. 294-297.
33. M. Protter and M. Elad, "Image sequence denoising via sparse and redundant representations," IEEE Trans. Image Process., vol. 18, no. 1, pp. 27-35, Jan. 2009.
34. S. G. Lingala and M. Jacob, "A blind compressive sensing frame work for accelerated dynamic MRI," in Proc. 9th IEEE Int. Symp. Biomed. Imag., Barcelona, Spain, May 2012, pp. 1060-1063.
35. S. G. Lingala and M. Jacob, "Blind compressive sensing dynamic MRI," IEEE Trans. Med. Imag., vol. 32, no. 6, pp. 1132-1145, Jun. 2013.
36. S. G. Lingala and M. Jacob, "Blind compressed sensing with sparse dictionaries for accelerated dynamic MRI," in Proc. 10th IEEE Int. Symp. Biomed. Imag., San Francisco, CA, Apr. 2013, pp. 5-8.
37. S. Ma,W. Yin, Y. Zhang, and A. Chakraborty, "An efficient algorithm for compressed MR imaging using total variation and wavelets," in Proc. 21st IEEE Conf. CVPR, Anchorage, AK, Jun. 2008, pp. 1-8.
38. J. Yang, Y. Zhang, and W. Yin, "A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 288-297, Apr. 2010.
39. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: Design of dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, pp. 4311-4322, Nov. 2006.
40. A. Chambolle, "Total variationminimization and a class of binary MRF models," in Proc. 5th Int. Workshop EMMCVPR, St. Augustine, FL, 2005, vol. 3757, pp. 136-152. (Pubitemid 43722931)
41. R. Rubinstein, M. Zibulevsky, and M. Elad, "Efficient implementation of the K-SVD algorithmusing batch orthogonalmatching pursuit". Dept. Comput. Sci., Technion-Israel Inst. Technol., Tech. Rep, 2008.
42. R. Rubinstein, KSVD-Box v13 and OMP-Box v10 Dept. Comput. Sci., Technion-Israel Inst. Technol., 2012 [Online]. Available: http://www.cs. technion.ac.il/~ronrubin/software.html
43. K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, "SENSE: Sensitivity encoding for fast MRI,"Magn. Reson. Med., vol. 42, no. 5, pp. 952-962, 1999. (Pubitemid 29521486)
44. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
45. M. Lustig, J. H. Lee, D. L. Donoho, and J. M. Pauly, "Faster imaging with randomly perturbed, undersampled spirals and L1 reconstruction," in Proc. 13th Annu. Meet. ISMRM, Miami Beach, FL, 2005, p. 685.
46. K. T. Block, M. Uecker, and J. Frahm, "Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint," Magn. Reson. Med., vol. 57, no. 6, pp. 1086-1098, Jun. 2007. (Pubitemid 46911293)
47. J. A. Fessler, "On NUFFT-based gridding for non-Cartesian MRI," J. Magn. Reson., vol. 188, no. 2, pp. 191-195, Oct. 2007. (Pubitemid 47562805)
48. M. Markl, K. Scheffler, and O. Bieri, "Analysis and compensation of eddy currents in balanced SSFP," Magn. Reson. Med., vol. 54, no. 1, pp. 129-137, Jul. 2005. (Pubitemid 40898405)
49. Dynamic MRI KAIST, Bio Imaging Processing Lab, 2012 [Online]. Available: http://bisp.kaist.ac.kr/ktFOCUSS.htm