Advertisement

Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI

Research Article

Abstract

Objectives

In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI.

Materials and methods

The process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI.

Results

With validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors.

Conclusion

The proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods.

Keywords

Compressed sensing Low-rank matrix completion Motion compensation Cardiac MRI 

Notes

Acknowledgements

The authors would like to thank Dr. Jong Ye for making the dynamic cardiac data available online: (http://bisp.kaist.ac.kr/ktFOCUSS.htm). This research has been supported by NSERC Discovery Grant RGPIN/239007.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

In this study, we used dynamic cardiac data available online (http://bisp.kaist.ac.kr/ktFOCUSS.htm). The Institutional Review Board of the University of Southern California approved the imaging protocols. Each subject was screened for magnetic resonance imaging risk factors and provided informed consent in accordance with institutional policy.

References

  1. 1.
    Sodickson DK, Griswold MA, Jakob PM, Edelman RR, Manning WJ (1999) Signal-to-noise ratio and signal-to-noise efficiency in SMASH imaging. Magn Reson Med 4(1):1009–1022CrossRefGoogle Scholar
  2. 2.
    Plein S, Bloomer TN, Ridgway JP, Jones TR, Bainbridge GJ, Sivananthan MU (2001) Steady-state free precession magnetic resonance imaging of the heart: comparison with segmented k-space gradient-echo imaging. J Magn Reson Imaging 14(3):230–236CrossRefPubMedGoogle Scholar
  3. 3.
    Lustig M, Santos JM, Donoho DL, Pauly JM (2006) k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In: Proceedings of the 13th annual meeting of international society for magnetic resonance in medicine (ISMRM), USA, p 2420Google Scholar
  4. 4.
    Jung H, Sung K, Nayak KS, Kim EY, Ye JC (2009) k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med 61(1):103–116CrossRefPubMedGoogle Scholar
  5. 5.
    Jung H, Park J, Yoo J, Ye JC (2010) Radial k-t FOCUSS for high-resolution cardiac cine MRI. Magn Reson Med 63:68–78PubMedGoogle Scholar
  6. 6.
    Tsao J, Boesigerp Pruessmann KP (2003) k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med 50:1031–1042CrossRefPubMedGoogle Scholar
  7. 7.
    Usman M, Prieto C, Schaeffter T, Batchelor P (2011) k-t group sparse: a method for accelerating dynamic MRI. Magn Reson Med 66(4):1163–1176CrossRefPubMedGoogle Scholar
  8. 8.
    Ravishankar S, Bresler Y (2011) MR image reconstruction from highly undersampled k-space data by dictionarylearning. IEEE Trans Med Imaging 30(5):1028–1041CrossRefPubMedGoogle Scholar
  9. 9.
    Feng L, Otazo R, Jung H, Jensen JH, Ye JC, Sodickson DK, Kim D (2011) Accelerated cardiac T2 mapping using breath-hold multiecho fast spin-echo pulse sequence with k-t FOCUSS. Magn Reson Med 65(6):1661–1669CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Candes E, Recht B (2009) Exact matrix completion via convex optimization. Found Comput Math 9:717–772CrossRefGoogle Scholar
  11. 11.
    Cai JF, Candes E, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982CrossRefGoogle Scholar
  12. 12.
    Liang ZP (2007) Spatiotemporal imaging with partially separable functions. In: Proceedings of IEEE international symposium biomedical imaging, pp 988–991Google Scholar
  13. 13.
    Haldar J, Liang ZP (2010) Spatiotemporal imaging with partially separable functions: a matrix recovery approach. In: Proceedings of IEEE international symposium biomedical imaging, pp 716–719Google Scholar
  14. 14.
    Lustig M, Elad M, Pauly J (2010) Calibrationless parallel imaging reconstruction by structured low-rank matrix completion. In: Proceedings of the 18th annual meeting of international society for magnetic resonance in medicine (ISMRM), p 2870Google Scholar
  15. 15.
    Lingala S, Hu Y, Dibella E, Jacob M (2011) Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging 30(5):1042–1054CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zhao B, Haldar JP, Christodoulou AG, Liang ZP (2012) Image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. IEEE Trans Med Imaging 31(9):1809–1820CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Candes E, Li X, Ma Y, Wright J (2011) Robust principal component analysis. J ACM 58(3):1–37CrossRefGoogle Scholar
  18. 18.
    Chandrasekaran V, Sanghavi S, Parrilo P, Willsky A (2011) Rank-sparsity incoherence for matrix decomposition. SIAM J Optim 21(2):572–596CrossRefGoogle Scholar
  19. 19.
    Gao H, Rapacchi S, Wang D, Moriarty J, Meehan C, Sayre J, Laub G, Finn P, Hu P (2012) Compressed sensing using prior rank, intensity and sparsity model (PRISM): applications in cardiac cine MRI. In: Proceedings of the 20th annual meeting of international society for magnetic resonance in medicine (ISMRM), p 2242Google Scholar
  20. 20.
    Otazo R, Candes E, Sodickson DK (2015) Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med 73:1125–1136CrossRefPubMedGoogle Scholar
  21. 21.
    Jung H, Ye JC (2010) Motion estimated and compensated compressed sensing dynamic magnetic resonance imaging: what we can learn from video compression techniques. Int J Imaging Syst Technol 20(2):81–98CrossRefGoogle Scholar
  22. 22.
    Asif MS, Hamilton L, Brummer M, Romberg J (2013) Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI. Magn Reson Med 70:800–812CrossRefPubMedGoogle Scholar
  23. 23.
    Usman M, Atkinson D, Odille F, Kolbitsch C, Vaillant G, Schaeffter T, Batchelor PG, Prieto C (2013) Motion corrected compressed sensing for freebreathing dynamic cardiac MRI. Magn Reson Med 70:504–516CrossRefPubMedGoogle Scholar
  24. 24.
    Royuela-del-Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C (2016) Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath-hold cardiac cine MRI. Magn Reson Med 75:1525–1536CrossRefPubMedGoogle Scholar
  25. 25.
    Sharif B, Bresler Y (2007) Physiologically improved NCAT phantom (PINCAT) enables in silico study of the effects of beat-to-beat variability on cardiac MR. In: Proceedings of international society for magnetic resonance in medicine (ISMRM), p 3418Google Scholar
  26. 26.
    Lingala SG, DiBella E, Jacob M (2015) Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI. IEEE Trans Med Imaging 34(1):72–85CrossRefPubMedGoogle Scholar
  27. 27.
    Lingala S, Jacob M (2013) Blind compressive sensing dynamic MRI. IEEE Trans Med Imaging 32(6):1132–1145CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Dowling J, Bourgeat P, Raffelt D, Fripp J, Greer PB, Patterson J, Denham J, Gupta S, Tang C, Stanwell P, Ourselin S, Salvado O (2009) Non-rigid correction of interleaving artefacts in pelvic MRI. In: Proceedings of SPIE medical imaging 2009: image processing, vol 7259Google Scholar

Copyright information

© ESMRMB 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringRyerson UniversityTorontoCanada
  2. 2.Department of Medical ImagingUniversity of Saskatchewan and Saskatoon Health RegionSaskatoonCanada

Personalised recommendations