Respiratory motion-corrected, compressively sampled dynamic MR image reconstruction by exploiting multiple sparsity constraints and phase correlation-based data binning

  • Faisal NajeebEmail author
  • Muhammad Usman
  • Ibtisam Aslam
  • Sohaib A. Qazi
  • Hammad Omer
Research Article



Cardiac magnetic resonance imaging (cMRI) is a standard method that is clinically used to evaluate the function of the human heart. Respiratory motion during a cMRI scan causes blurring artefacts in the reconstructed images. In conventional MRI, breath holding is used to avoid respiratory motion artefacts, which may be difficult for cardiac patients.

Materials and Methods

This paper proposes a method in which phase correlation-based binning, followed by image registration-based sparsity along with spatio-temporal sparsity, is incorporated into the standard low rank + sparse (L+S) reconstruction for free-breathing cardiac cine MRI. The proposed method is validated on clinical data and simulated free-breathing cardiac cine data for different acceleration factors (AFs). The reconstructed images are analysed using visual assessment, artefact power (AP) and root-mean-square error (RMSE). The results of the proposed method are compared with the contemporary motion-corrected compressed sensing (MC-CS) method given in the literature.


Our results show that the proposed method successfully reconstructs the motion-corrected images from respiratory motion-corrupted, compressively sampled cardiac cine MR data, e.g., there is 26% and 24% improvement in terms of AP and RMSE values, respectively, at AF = 4 and 20% and 16.04% improvement in terms of AP and RMSE values, respectively, at AF = 8 in the reconstruction results from the proposed method for the cardiac phantom cine data.


The proposed method achieves significant improvement in the AP and RMSE values at different AFs for both the phantom and in vivo data.




Compliance with ethical standards

Conflict of interest

Faisal Najeeb declares that he has no conflict of interest. Muhammad Usman declares that he has no conflict of interest. Ibtisam Aslam declares that he has no conflict of interest. Sohaib Ayaz Qazi declares that he has no conflict of interest. Hammad Omer declares that he has no conflict of interest.

Ethical approval

All the procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all the individual participants included in the study before data acquisition.


  1. 1.
    Usman M et al (2013) Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magn Reson Med 70(2):504–516CrossRefGoogle Scholar
  2. 2.
    Hamilton J, Franson D, Seiberlich N (2017) Recent advances in parallel imaging for MRI. Prog Nucl Magn Reson Spectrosc 101:71–95CrossRefGoogle Scholar
  3. 3.
    Ricardo O et al (2010) Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 64(3):767–776CrossRefGoogle Scholar
  4. 4.
    Feng L et al (2013) Highly accelerated real-time cardiac cine MRI using k–t SPARSE-SENSE. Magn Reson Med 70(1):64–74CrossRefGoogle Scholar
  5. 5.
    Anagha D et al (2012) Parallel MR imaging. J Magn Reson Imaging 36(1):55–72CrossRefGoogle Scholar
  6. 6.
    Ahmed AH et al (2017) Motion correction-based reconstruction method for compressively sampled cardiac MR imaging. Magn Reson Imaging 36:159–166CrossRefGoogle Scholar
  7. 7.
    Barkhausen J et al (2002) Assessment of ventricular function with single breath-hold real-time steady-state free precession cine MR imaging. Am J Roentgenol 178(3):731–735CrossRefGoogle Scholar
  8. 8.
    Kellman P et al (2009) High spatial and temporal resolution cardiac cine MRI from retrospective reconstruction of data acquired in real time using motion correction and resorting. Magn Reson Med 62(6):1557–1564CrossRefGoogle Scholar
  9. 9.
    Axel L, Otazo R (2016) Accelerated MRI for the assessment of cardiac function. Br J Radiol 89(1063):20150655CrossRefGoogle Scholar
  10. 10.
    Setser RM, Fischer SE, Lorenz CH (2000) Quantification of left ventricular function with magnetic resonance images acquired in real time. J Magn Reson Imaging 12(3):430–438CrossRefGoogle Scholar
  11. 11.
    Kellman P, McVeigh ER (2001) Adaptive sensitivity encoding incorporating temporal filtering (TSENSE). Magn Reson Med 45(5):846–852CrossRefGoogle Scholar
  12. 12.
    Breuer FA et al (2005) Dynamic auto-calibrated parallel imaging using temporal GRAPPA (TGRAPPA). Magn Reson Med 53(4):981–985CrossRefGoogle Scholar
  13. 13.
    Liu J et al (2017) Highly-accelerated self-gated free-breathing 3D cardiac cine MRI: validation in assessment of left ventricular function. Magn Reson Mater Phy 30(4):337–346CrossRefGoogle Scholar
  14. 14.
    McLeish K et al (2002) A study of the motion and deformation of the heart due to respiration. IEEE Trans Med Imaging 21(9):1142–1150CrossRefGoogle Scholar
  15. 15.
    Ding Yu et al (2011) A new approach to auto-calibrated dynamic parallel imaging based on the Karhunen–Loeve transform: KL-TSENSE and KL-TGRAPPA. Magn Reson Med 65(6):1786–1792CrossRefGoogle Scholar
  16. 16.
    Hansen MS et al (2012) Retrospective reconstruction of high temporal resolution cine images from real-time MRI using iterative motion correction. Magn Reson Med 68(3):741–750CrossRefGoogle Scholar
  17. 17.
    Bilal M et al (2018) Respiratory motion correction for compressively sampled free breathing cardiac MRI using smooth-norm approximation. Int J Biomed Imaging 2018Google Scholar
  18. 18.
    Kellman P, Chefdhotel C, Lorenz CH, Mancini C, Arai AE, McVeigh ER (2008) Fully automatic, retrospective enhancement of real-time acquired cardiac cine MR images using image-based navigators and respiratory motion-corrected averaging. Magn Res Med 59:771–778CrossRefGoogle Scholar
  19. 19.
    Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R (2016) XD-GRASP: golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 75(2):775–788CrossRefGoogle 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(3):1125–1136CrossRefGoogle Scholar
  21. 21.
    Foroosh H, Zerubia JB, Berthod M (2002) Extension of phase correlation to subpixel registration. IEEE Trans Image Process 11(3):188–200CrossRefGoogle Scholar
  22. 22.
    Vercauteren T et al (2009) Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45(1):S61–S72CrossRefGoogle Scholar
  23. 23.
    Kroon DJ, Cornelis HS (2009) MRI modality transformation in demon registration. 2009 IEEE international symposium on biomedical imaging: from nano to macro. IEEEGoogle Scholar
  24. 24.
    Thirion J-P (1998) Image matching as a diffusion process: an analogy with Maxwell's demons. Med Image Anal 2(3):243–260CrossRefGoogle Scholar
  25. 25.
    (ISMRM 2014) Motion-guided low-rank plus sparse (L+S) reconstruction for free-breathing dynamic MRI. Accessed 23 Jul 2018
  26. 26.
    Jung H et al (2009) k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. Magn Reson Med 61(1):103–116CrossRefGoogle Scholar
  27. 27.
    Wissmann L et al (2014) MRXCAT: realistic numerical phantoms for cardiovascular magnetic resonance. J Cardiovasc Magn Reson 16(1):63CrossRefGoogle Scholar
  28. 28.
    Fessler JA (2007) On NUFFT-based gridding for non-Cartesian MRI. J Magn Reson 188(2):191–195CrossRefGoogle Scholar
  29. 29.
    Aslam I, Najeeb F, Omer H (2018) Accelerating MRI using GROG gridding followed by ESPIRiT for non-Cartesian trajectories. Appl Magn Reson 49(1):107–124CrossRefGoogle Scholar

Copyright information

© European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2019

Authors and Affiliations

  • Faisal Najeeb
    • 1
    Email author
  • Muhammad Usman
    • 2
  • Ibtisam Aslam
    • 1
  • Sohaib A. Qazi
    • 1
  • Hammad Omer
    • 1
  1. 1.Department of Electrical and Computer EngineeringCOMSATS University IslamabadIslamabadPakistan
  2. 2.Department of Computer ScienceUniversity College LondonLondonUK

Personalised recommendations