Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration

  • Eve Piekarski
  • Teodora Chitiboi
  • Rebecca Ramb
  • Larry A. LatsonJr
  • Puneet Bhatla
  • Li Feng
  • Leon Axel
Research Article



Residual respiratory motion degrades image quality in conventional cardiac cine MRI (CCMRI). We evaluated whether a free-breathing (FB) radial imaging CCMRI sequence with compressed sensing reconstruction [extradimensional (e.g. cardiac and respiratory phases) golden-angle radial sparse parallel, or XD-GRASP] could provide better image quality than a conventional Cartesian breath-held (BH) sequence in an unselected population of patients undergoing clinical CCMRI.

Materials and methods

One hundred one patients who underwent BH and FB imaging in a midventricular short-axis plane at a matching location were included. Visual and quantitative image analysis was performed by two blinded experienced readers, using a five-point qualitative scale to score overall image quality and visual signal-to-noise ratio (SNR) grade, with measures of noise and sharpness. End-diastolic and end-systolic left ventricular areas were also measured and compared for both BH and FB images.


Image quality was generally better with the BH cines (overall quality grade for BH vs FB images 4 vs 2.9, p < 0.001; noise 0.06 vs 0.08 p < 0.001; SNR grade 4.1 vs 3, p < 0.001), except for sharpness (p = 0.48). There were no significant differences between BH and FB images regarding end-diastolic or end-systolic areas (p = 0.35 and p = 0.12). Eighteen of the 101 patients had poor BH image quality (grade 1 or 2). In this subgroup, the quality of the FB images was better (p = 0.0032), as was the SNR grade (p = 0.003), but there were no significant differences regarding noise and sharpness (p = 0.45 and p = 0.47).


Although FB XD-GRASP CCMRI was visually inferior to conventional BH CCMRI in general, it provided improved image quality in the subgroup of patients with respiratory-motion-induced artifacts on BH images.


Magnetic resonance imaging Cardiac imaging techniques Cardiac-gated imaging techniques Cine magnetic resonance imaging Image reconstruction 


Author contributions

EP participated in the data collection and the data analysis and wrote the manuscript. TC participated in the data collection and the data analysis and reviewed the manuscript. RR participated in the data analysis and reviewed the manuscript. LAL participated in the data collection and reviewed the manuscript. PB participated in the data collection and reviewed the manuscript. LF participated in the protocol development and reviewed the manuscript. LA designed the project development, participated in the data analysis, and redacted and reviewed the manuscript.

Compliance with ethical standards


This study was funded by the NIH (Grant Number NIH R21-EB109595-01).

Conflict of interest

The authors declare that they have no competing interests.

Research involving human participants and informed consent

This retrospective study was approved by our institutional review board and was performed according to standards of the Health Insurance Portability and Accountability Act. Documentation of consent was waived.


  1. 1.
    Pennell DJ, Sechtem UP, Higgins CB et al (2004) Clinical indications for cardiovascular magnetic resonance (CMR): consensus panel report. Eur Heart J 25(21):1940–1965CrossRefPubMedGoogle Scholar
  2. 2.
    Hundley WG, Bluemke DA, Finn JP et al (2010) ACCF/ACR/AHA/NASCI/SCMR 2010 expert consensus document on cardiovascular magnetic resonance: a report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents. J Am Coll Cardiol 55(23):2614–2662CrossRefPubMedGoogle Scholar
  3. 3.
    Ferreira PF, Gatehouse PD, Mohiaddin RH, Firmin DN (2013) Cardiovascular magnetic resonance artefacts. J Cardiovasc Magn Reson 15:41CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Saloner D, Liu J, Haraldsson H (2015) MR physics in practice: how to optimize acquisition quality and time for cardiac MR imaging. Magn Reson Imaging Clin N Am 23(1):1–6CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    van Heeswijk RB, Bonanno G, Coppo S, Coristine A, Kober T, Stuber M (2012) Motion compensation strategies in magnetic resonance imaging. Crit Rev Biomed Eng 40(2):99–119CrossRefPubMedGoogle Scholar
  6. 6.
    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):775–788CrossRefPubMedGoogle Scholar
  7. 7.
    Chen C, Li Y, Axel L, Huang J (2016) Real time dynamic MRI by exploiting spatial and temporal sparsity. Magn Reson Imaging 34(4):473–482CrossRefPubMedGoogle Scholar
  8. 8.
    Wech T, Pickl W, Tran-Gia J, Ritter C, Beer M, Hahn D, Köstler H (2014) Whole-heart cine MRI in a single breath-hold—a compressed sensing accelerated 3D acquisition technique for assessment of cardiac function. Rofo 186(1):37–41PubMedGoogle Scholar
  9. 9.
    Weber MA (2013) Magnetic resonance imaging of the skeletal musculature. Springer, BerlinGoogle Scholar
  10. 10.
    Glover GH, Pauly JM (1992) Projection reconstruction techniques for reduction of motion effects in MRI. Magn Reson Med 28(2):275–289CrossRefPubMedGoogle Scholar
  11. 11.
    Rudin LO, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60(1–4):259–268CrossRefGoogle Scholar
  12. 12.
    Adluru G, Awate SP, Tasdizen T, Whitaker RT, Dibella EV (2007) Temporally constrained reconstruction of dynamic cardiac perfusion MRI. Magn Reson Med 57(6):1027–1036CrossRefPubMedGoogle Scholar
  13. 13.
    Block KT, Uecker M, Frahm J (2007) Undersampled radial MRI with multiple coils. iterative image reconstruction using a total variation constraint. Magn Reson Med 57(6):1086–1098CrossRefPubMedGoogle Scholar
  14. 14.
    Lustig M, Santos M, Donoho D, Pauly JM (2006) k-t SPARSE: high frame rate dynamic MRI exploiting spatio-temporal sparsity. In: Proceedings of the 13th Annual Meeting of ISMRM, vol 50, No. 5. Seattle, p 2420.
  15. 15.
    Thompson RB, McVeigh ER (2006) Cardiorespiratory-resolved magnetic resonance imaging: measuring respiratory modulation of cardiac function. Magn Reson Med 56(6):1301–1310CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lustig M, Donoho D, Santos J, Pauly JM (2008) Compressed sensing MRI. IEEE Signal Process Mag 25(2):72–82CrossRefGoogle Scholar
  17. 17.
    Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195CrossRefPubMedGoogle Scholar
  18. 18.
    Babacan SD, Peng X, Wang XP, Do MN, Liang ZP (2011) Reference-guided sparsifying transform design for compressive sensing MRI. Conf Proc IEEE Eng Med Biol Soc 2011:5718–5721. doi: 10.1109/IEMBS.2011.6091384 PubMedPubMedCentralGoogle Scholar
  19. 19.
    Smith DS, Li X, Abramson RG, Quarles CC, Yankeelov TE, Welch EB (2013) Potential of compressed sensing in quantitative MR imaging of cancer. Cancer Imaging 13(4):633–644CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Zibetti MVW, De Pierro AR (2017) Improving compressive sensing in MRI with separate magnitude and phase priors. Multidim Syst Sign Process 28:1109. doi: 10.1007/s11045-016-0383-6 CrossRefGoogle Scholar
  21. 21.
    Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30CrossRefGoogle Scholar
  22. 22.
    Ahmad R, Ding Y, Simonetti OP (2015) Edge sharpness assessment by parametric modeling: application to magnetic resonance imaging. Concepts Magn Reson Part A 44(3):138–149CrossRefGoogle Scholar
  23. 23.
    Xue Y, Yu J, Kang HS, Englander S, Rosen MA, Song HK (2012) Automatic coil selection for streak artifact reduction in radial MRI. Magn Reson Med 67(2):470–476CrossRefPubMedGoogle Scholar
  24. 24.
    Chan RW, Ramsay EA, Cheung EY, Plewes DB (2012) The influence of radial undersampling schemes on compressed sensing reconstruction in breast MRI. Magn Reson Med 67(2):363–377CrossRefPubMedGoogle Scholar
  25. 25.
    Riffel P, Zoellner FG, Budjan J et al (2016) “One-stop shop”: free-breathing dynamic contrast-enhanced magnetic resonance imaging of the kidney using iterative reconstruction and continuous golden-angle radial sampling. Investig Radiol 51(11):714–719CrossRefGoogle Scholar
  26. 26.
    Lin W, Huang F, Simonotto E, Duensing GR, Reykowski A (2012) Off-resonance artifacts correction with convolution in k-space (ORACLE). Magn Reson Med 67:1547–1555CrossRefPubMedGoogle Scholar
  27. 27.
    Wei Li W, Storey P, Chen Q, Li BSY, Prasad PV, Edelman RR (2004) Dark flow artifacts with steady-state free precession cine MR technique: causes and implications for cardiac MR imaging. Radiology 230:569–575CrossRefPubMedGoogle Scholar
  28. 28.
    Pang J, Sharif B, Fan Z et al (2014) ECG and navigator-free four-dimensional whole-heart coronary MRA for simultaneous visualization of cardiac anatomy and function. Magn Reson Med. doi: 10.1002/mrm.25450 Google Scholar
  29. 29.
    Contijoch F, Iyer SK, Pilla JJ et al (2016) Self-gated MRI of multiple beat morphologies in the presence of arrhythmias. Magn Reson Med. doi: 10.1002/mrm.26381 PubMedGoogle Scholar
  30. 30.
    Piekarski E, Chitiboi T, Ramb R, Feng L, Axel L (2016) Use of self-gated radial cardiovascular magnetic resonance to detect and classify arrhythmias (atrial fibrillation and premature ventricular contraction). J Cardiovasc Magn Reson 18(1):83CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Huang RY, Dung LR (2016) Measurement of heart rate variability using off-the-shelf smart phones. Biomed Eng Online 15:11CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Ruan X, Liu C, Li P (2011) Detection of atrial fibrillation using R-R interval signal. In: 4th international conference on biomedical engineering and informatics, BMEI 2011, Shanghai, China, October 15–17, 2011, at Shanghai, China. doi:  10.1109/BMEI.2011.6098492
  33. 33.
    Francone M, Dymarkowski S, Kalantzi M, Bogaert J (2005) Real-time cine MRI of ventricular septal motion: a novel approach to assess ventricular coupling. J Magn Reson Imaging 21(3):305–309CrossRefPubMedGoogle Scholar

Copyright information

© ESMRMB 2017

Authors and Affiliations

  • Eve Piekarski
    • 1
    • 2
  • Teodora Chitiboi
    • 1
    • 2
  • Rebecca Ramb
    • 1
    • 2
  • Larry A. LatsonJr
    • 3
  • Puneet Bhatla
    • 3
  • Li Feng
    • 1
    • 2
  • Leon Axel
    • 1
    • 2
    • 3
  1. 1.Center for Advanced Imaging Innovation and Research (CAI2R), Department of RadiologyNew York University School of MedicineNew YorkUSA
  2. 2.Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University School of MedicineNew YorkUSA
  3. 3.Department of RadiologyNew York University Langone Medical CenterNew YorkUSA

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