Skip to main content
SpringerLink
Log in

Influence of the cardiac cycle on time–intensity curves using multislice dynamic magnetic resonance perfusion

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

Flow and pressure variations cause potential changes in magnetic resonance imaging (MRI) signal intensity across the cardiac cycle. Nevertheless, cardiac dynamic contrast-enhanced (perfusion) MRI is performed and analyzed regardless of the cardiac phase. We investigate whether the cardiac phase impacts myocardial and left ventricle (LV) cavity time intensity curves (TICs) at rest and during vasodilatation. Fifteen healthy volunteers (seven females, eight males; mean age: 32.5 ± 9.3 years; age range: 19–49 years) were included in this prospective study. They underwent four separate short-axis multislice (apical, mid and basal) LV perfusion MRI, with different electrocardiogram-triggering during normal vasotone and adenosine-stress. TIC parameters were extracted from the myocardium and the LV cavity. General linear mixed model analyses were used to evaluate their variability according to vasotone, cardiac phase and slice-position. Maximal enhancement and normalized Steepest slopes were higher at stress than at rest (p values <0.001). A similar trend towards higher inflow was shown on systole versus diastole in the LV cavity and diastole versus systole in the myocardium (p < 0.05).These TIC parameters were slice-position dependent, as the inflow decreased from the base to the apex in the LV, and peaked on the mid-slice for the myocardium. There are significant variability of both the LV and the myocardial TICs, with respect to the cardiac cycle phase and the slice position where imaging actually takes place. These appeal to measurement standardization for a better intra- and inter-study reproducibility.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Ishida M, Kato S, Sakuma H (2009) Cardiac MRI in ischemic heart disease. Circ J 73(9):1577–1588

    Article  PubMed  Google Scholar 

  2. Nagel E et al (2003) Magnetic resonance perfusion measurements for the noninvasive detection of coronary artery disease. Circulation 108(4):432–437

    Article  PubMed  Google Scholar 

  3. Gerber BL et al (2008) Myocardial first-pass perfusion cardiovascular magnetic resonance: history, theory, and current state of the art. J Cardiovasc Magn Reson 10:18

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kellman P, Arai AE (2007) Imaging sequences for first pass perfusion—a review. J Cardiovasc Magn Reson 9(3):525–537

    Article  PubMed  Google Scholar 

  5. Lerman BB, Belardinelli L (1991) Cardiac electrophysiology of adenosine. Basic and clinical concepts. Circulation 83(5):1499–1509

    Article  PubMed  CAS  Google Scholar 

  6. Gould KL, Kirkeeide RL, Buchi M (1990) Coronary flow reserve as a physiologic measure of stenosis severity. J Am Coll Cardiol 15(2):459–474

    Article  PubMed  CAS  Google Scholar 

  7. Chilian WM, Marcus ML (1982) Phasic coronary blood flow velocity in intramural and epicardial coronary arteries. Circ Res 50(6):775–781

    Article  PubMed  CAS  Google Scholar 

  8. Radjenovic A et al (2010) Estimates of systolic and diastolic myocardial blood flow by dynamic contrast-enhanced MRI. Magn Reson Med 64(6):1696–1703

    Article  PubMed  Google Scholar 

  9. Motwani M et al (2012) Systolic versus diastolic acquisition in myocardial perfusion MR imaging. Radiology 262(3):816–823

    Article  PubMed  Google Scholar 

  10. Larghat A et al (2012) Endocardial and epicardial myocardial perfusion determined by semi-quantitative and quantitative myocardial perfusion magnetic resonance. Int J Cardiovasc Imaging 28(6):1499–1511

    Article  PubMed  Google Scholar 

  11. Austin RE Jr et al (1990) Profound spatial heterogeneity of coronary reserve. Discordance between patterns of resting and maximal myocardial blood flow. Circ Res 67(2):319–331

    Article  PubMed  Google Scholar 

  12. Diamond GA, Forrester JS (1979) Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med 300(24):1350–1358

    Article  PubMed  CAS  Google Scholar 

  13. Deshpande VS et al (2003) Reduction of transient signal oscillations in true-FISP using a linear flip angle series magnetization preparation. Magn Reson Med 49(1):151–157

    Article  PubMed  Google Scholar 

  14. Boudoulas H et al (1981) Linear relationship between electrical systole, mechanical systole, and heart rate. Chest 80(5):613–617

    Article  PubMed  CAS  Google Scholar 

  15. Jerosch-Herold M et al (2004) Analysis of myocardial perfusion MRI. J Magn Reson Imaging 19(6):758–770

    Article  PubMed  Google Scholar 

  16. Thompson HK Jr et al (1964) Indicator transit time considered as a gamma variate. Circ Res 14:502–515

    Article  PubMed  Google Scholar 

  17. Klocke FJ et al (2001) Limits of detection of regional differences in vasodilated flow in viable myocardium by first-pass magnetic resonance perfusion imaging. Circulation 104(20):2412–2416

    Article  PubMed  CAS  Google Scholar 

  18. Edelman RR (1992) Basic principles of magnetic resonance angiography. Cardiovasc Intervent Radiol 15(1):3–13

    Article  PubMed  CAS  Google Scholar 

  19. Parkka JP et al (2006) Comparison of MRI and positron emission tomography for measuring myocardial perfusion reserve in healthy humans. Magn Reson Med 55(4):772–779

    Article  PubMed  Google Scholar 

  20. Peeters F et al (2004) Inflow correction of hepatic perfusion measurements using T1-weighted, fast gradient-echo, contrast-enhanced MRI. Magn Reson Med 51(4):710–717

    Article  PubMed  Google Scholar 

  21. Ibrahim T et al (2002) Assessment of coronary flow reserve: comparison between contrast-enhanced magnetic resonance imaging and positron emission tomography. J Am Coll Cardiol 39(5):864–870

    Article  PubMed  Google Scholar 

  22. Nchimi A et al (2014) Myocardial dynamic contrast-enhanced Mr: vascular diseases and beyond. JBR-BTR 97(1):3–10

    PubMed  CAS  Google Scholar 

  23. Wu EX et al (2004) Mapping cyclic change of regional myocardial blood volume using steady-state susceptibility effect of iron oxide nanoparticles. J Magn Reson Imaging 19(1):50–58

    Article  PubMed  Google Scholar 

  24. Ivancevic MK et al (2003) Inflow effect in first-pass cardiac and renal MRI. J Magn Reson Imaging 18(3):372–376

    Article  PubMed  Google Scholar 

  25. Zierler KL (1962) Theoretical basis of indicator-dilution methods for measuring flow and volume. Circ Res 10:393–407

    Article  Google Scholar 

  26. Lee DC, Johnson NP (2009) Quantification of absolute myocardial blood flow by magnetic resonance perfusion imaging. JACC Cardiovasc Imaging 2(6):761–770

    Article  PubMed  Google Scholar 

  27. Muehling OM et al (2004) Regional heterogeneity of myocardial perfusion in healthy human myocardium: assessment with magnetic resonance perfusion imaging. J Cardiovasc Magn Reson 6(2):499–507

    Article  PubMed  Google Scholar 

  28. Slavin G, Fung M (2014) Electromechanical analysis of optimal trigger delays for cardiac MRI. J Cardiovasc Magn Reson 16(Suppl 1):P73

    Article  PubMed Central  Google Scholar 

  29. Larghat AM et al (2013) Reproducibility of first-pass cardiovascular magnetic resonance myocardial perfusion. J Magn Reson Imaging 37(4):865–874

    Article  PubMed  Google Scholar 

  30. Wansapura J et al (2006) Cyclic variation of T1 in the myocardium at 3 T. Magn Reson Imaging 24(7):889–893

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank Amir-Samy Aouchria, MD, Adelin Albert, PhD and Laurence Seidel MSc for statistical advice; Ali Barah, MD, Paul Magotteaux, MD, the volunteers and the whole team of cardiac magnetic resonance imaging department at the Saint-Joseph hospital of Liège for supporting the project.

Conflict of interest

None.

Author information

Authors and Affiliations

  1. Department of Medical Imaging, University Hospital of Liège (CHU), GIGA Cardiovascular Sciences, Domaine Universitaire du Sart Tilman B35, 4000, Liège, Belgium

    Alain Nchimi

  2. Department of Medical Imaging, Centre Hospitalier Chrétien (CHC), Rue de Hesbaye, 75, 4000, Liège, Belgium

    Isabelle Mancini

  3. Medical Research Department, GCS-GHICL, Groupement des Hôpitaux de l’Institut Catholique de Lille (GHICL), Université Catholique de Lille, Lille, France

    Thomas K. Y. Broussaud

  4. Hôpital Saint Philibert, Université Lille Nord de France, 115 Rue du Grand But, BP 249, 59462, Lomme Cedex, France

    Thomas K. Y. Broussaud

Authors
  1. Alain Nchimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isabelle Mancini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas K. Y. Broussaud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alain Nchimi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nchimi, A., Mancini, I. & Broussaud, T.K.Y. Influence of the cardiac cycle on time–intensity curves using multislice dynamic magnetic resonance perfusion. Int J Cardiovasc Imaging 30, 1347–1355 (2014). https://doi.org/10.1007/s10554-014-0466-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-014-0466-0

Keywords

Search

Navigation