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Impact of baseline calibration on semiquantitative assessment of myocardial perfusion reserve by adenosine stress MRI

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Abstract

In this study, we sought to investigate the impact of baseline calibration, which is used in quantitative cardiac MRI perfusion analysis to correct for surface coil inhomogeneity and noise, on myocardial perfusion reserve index (MPRI) and its contribution to previously reported paradoxical low MPRI < 1.0 in patients with unobstructed coronary arteries. Semiquantitative perfusion analysis was performed in 20 patients with unobstructed coronary arteries undergoing stress/rest perfusion CMR and in ten patients undergoing paired rest perfusion CMR. The following baseline calibration settings were compared: (1) baseline division, (2) baseline subtraction and (3) no baseline calibration. In uncalibrated analysis, we observed ~ 20% segmental dispersion of signal intensity (SI)-over-time curves. Both baseline subtraction and baseline division reduced relative dispersion of t0-SI (p < 0.001), but only baseline division corrected for dispersion of peak-SI and maximum upslope also (p < 0.001). In the assessment of perfusion indices, however, baseline division resulted in paradoxical low MPRI (1.01 ± 0.23 vs. 1.63 ± 0.38, p < 0.001) and rest perfusion index (RPI 0.54 ± 0.07 vs. 0.94 ± 0.12, p < 0.001), respectively. This was due to a reversed ratio of blood-pool and myocardial baseline-SI before the second perfusion study caused by circulating contrast agent from the first injection. In conclusion, baseline division reliably corrects for inhomogeneity of the surface coil sensitivity profile facilitating comparisons of regional myocardial perfusion during hyperemia or at rest. However, in the assessment of MPRI, baseline division can lead to paradoxical low results (even MPRI < 1.0 in patients with unobstructed coronary arteries) potentially mimicking severely impaired perfusion reserve. Thus, in the assessment of MPRI we propose to waive baseline calibration.

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Abbreviations

CAD:

Coronary artery disease

CI:

Confidence interval

CMD:

Coronary microvascular disease

CMR:

Cardiac MRI

CoV:

Coefficient of variation

ECG:

Electrocardiogram

EDV:

End-diastolic volume

ICC:

Intra-class correlation coefficient

LVEF:

LV ejection fraction

ESV:

End-systolic volume

LGE:

Late gadolinium enhancement

LV:

Left ventricular

MPRI:

Myocardial perfusion reserve index

MRI:

Magnetic resonance imaging

PD:

Proton density

RPI:

Rest perfusion index

RU:

Relative upslope

SI:

Signal intensity

SD:

Standard deviation

References

  1. Kiaos A, Tziatzios I, Hadjimiltiades S, Karvounis C, Karamitsos TD (2018) Diagnostic performance of stress perfusion cardiac magnetic resonance for the detection of coronary artery disease: a systematic review and meta-analysis. Int J Cardiol 252:229–233

    Article  Google Scholar 

  2. Liu A, Wijesurendra RS, Liu JM et al (2018) Diagnosis of microvascular angina using cardiac magnetic resonance. J Am Coll Cardiol 71(9):969–979

    Article  CAS  Google Scholar 

  3. Thomson LE, Wei J, Agarwal M et al (2015) Cardiac magnetic resonance myocardial perfusion reserve index is reduced in women with coronary microvascular dysfunction. A National Heart, Lung, and Blood Institute-sponsored study from the Women's Ischemia Syndrome Evaluation. Circ Cardiovasc Imaging 8(4):e002481

    Article  Google Scholar 

  4. Zorach B, Shaw PW, Bourque J et al (2018) Quantitative cardiovascular magnetic resonance perfusion imaging identifies reduced flow reserve in microvascular coronary artery disease. J Cardiovasc Magn Reson 20(1):14

    Article  Google Scholar 

  5. Utz W, Niendorf T, Wassmuth R, Messroghli D, Dietz R, Schulz-Menger J (2007) Contrast-dose relation in first-pass myocardial MR perfusion imaging. J Magn Reson Imaging 25(6):1131–1135

    Article  Google Scholar 

  6. Mordini FE, Haddad T, Hsu LY et al (2014) Diagnostic accuracy of stress perfusion CMR in comparison with quantitative coronary angiography: fully quantitative, semiquantitative, and qualitative assessment. JACC Cardiovasc Imaging 7(1):14–22

    Article  Google Scholar 

  7. Hoffmann MH, Schmid FT, Jeltsch M et al (2005) Multislice MR first-pass myocardial perfusion imaging: impact of the receiver coil array. J Magn Reson Imaging 21(3):310–316

    Article  Google Scholar 

  8. Murakami JW, Hayes CE, Weinberger E (1996) Intensity correction of phased-array surface coil images. Magn Reson Med 35(4):585–590

    Article  CAS  Google Scholar 

  9. Jerosch-Herold M, Seethamraju RT, Swingen CM, Wilke NM, Stillman AE (2004) Analysis of myocardial perfusion MRI. J Magn Reson Imaging 19(6):758–770

    Article  Google Scholar 

  10. Shufelt CL, Thomson LE, Goykhman P et al (2013) Cardiac magnetic resonance imaging myocardial perfusion reserve index assessment in women with microvascular coronary dysfunction and reference controls. Cardiovasc Diagn Ther 3(3):153–160

    PubMed  PubMed Central  Google Scholar 

  11. Ibrahim T, Nekolla SG, Schreiber K 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  Google Scholar 

  12. Lockie T, Ishida M, Perera D et al (2011) High-resolution magnetic resonance myocardial perfusion imaging at 3.0-Tesla to detect hemodynamically significant coronary stenoses as determined by fractional flow reserve. J Am Coll Cardiol 57(1):70–75

    Article  Google Scholar 

  13. Rieber J, Huber A, Erhard I et al (2006) Cardiac magnetic resonance perfusion imaging for the functional assessment of coronary artery disease: a comparison with coronary angiography and fractional flow reserve. Eur Heart J 27(12):1465–1471

    Article  Google Scholar 

  14. Costa MA, Shoemaker S, Futamatsu H et al (2007) Quantitative magnetic resonance perfusion imaging detects anatomic and physiologic coronary artery disease as measured by coronary angiography and fractional flow reserve. J Am Coll Cardiol 50(6):514–522

    Article  Google Scholar 

  15. Al-Saadi N, Nagel E, Gross M et al (2000) Improvement of myocardial perfusion reserve early after coronary intervention: assessment with cardiac magnetic resonance imaging. J Am Coll Cardiol 36(5):1557–1564

    Article  CAS  Google Scholar 

  16. Jahnke C, Gebker R, Manka R, Schnackenburg B, Fleck E, Paetsch I (2010) Navigator-gated 3D blood oxygen level-dependent CMR at 3.0-T for detection of stress-induced myocardial ischemic reactions. JACC Cardiovasc Imaging 3(4):375–384

    Article  Google Scholar 

  17. Cullen JH, Horsfield MA, Reek CR, Cherryman GR, Barnett DB, Samani NJ (1999) A myocardial perfusion reserve index in humans using first-pass contrast-enhanced magnetic resonance imaging. J Am Coll Cardiol 33(5):1386–1394

    Article  CAS  Google Scholar 

  18. Anagnostopoulos C, Almonacid A, El Fakhri G et al (2008) Quantitative relationship between coronary vasodilator reserve assessed by 82Rb PET imaging and coronary artery stenosis severity. Eur J Nucl Med Mol Imaging 35(9):1593–1601

    Article  Google Scholar 

  19. Akinboboye OO, Idris O, Chou RL, Sciacca RR, Cannon PJ, Bergmann SR (2001) Absolute quantitation of coronary steal induced by intravenous dipyridamole. J Am Coll Cardiol 37(1):109–116

    Article  CAS  Google Scholar 

  20. Raman SV, Phatak K, Hoyle JC et al (2011) Impaired myocardial perfusion reserve and fibrosis in Friedreich ataxia: a mitochondrial cardiomyopathy with metabolic syndrome. Eur Heart J 32(5):561–567

    Article  Google Scholar 

  21. Narang A, Mor-Avi V, Bhave NM et al (2016) Large high-density lipoprotein particle number is independently associated with microvascular function in patients with well-controlled low-density lipoprotein concentration: a vasodilator stress magnetic resonance perfusion study. J Clin Lipidol 10(2):314–322

    Article  Google Scholar 

  22. Narang A, Blair JE, Patel MB et al (2018) Myocardial perfusion reserve and global longitudinal strain as potential markers of coronary allograft vasculopathy in late-stage orthotopic heart transplantation. Int J Cardiovasc Imaging 34(10):1607–1617

    Article  Google Scholar 

  23. Mavrogeni S, Bratis K, van Wijk K et al (2012) Myocardial perfusion-fibrosis pattern in systemic sclerosis assessed by cardiac magnetic resonance. Int J Cardiol 159(3):e56–e58

    Article  Google Scholar 

  24. Mavrogeni S, Bratis K, Papachristou P et al (2013) Stress perfusion-fibrosis cardiac magnetic resonance detects early heart involvement in young asymptomatic, homozygous familial hyperlipidemia with normal routine non-invasive evaluation. Int J Cardiol 168(4):4570–4572

    Article  Google Scholar 

  25. Mavrogeni S, Bratis K, Koutsogeorgopoulou L et al (2017) Myocardial perfusion in peripheral Raynaud's phenomenon. Evaluation using stress cardiovascular magnetic resonance. Int J Cardiol 228:444–448

    Article  Google Scholar 

  26. Bratis K, Kattamis A, Athanasiou K et al (2013) Abnormal myocardial perfusion-fibrosis pattern in sickle cell disease assessed by cardiac magnetic resonance imaging. Int J Cardiol 166(3):e75–e76

    Article  Google Scholar 

  27. Bratis K, Chiribiri A, Hussain T et al (2015) Abnormal myocardial perfusion in Kawasaki disease convalescence. JACC Cardiovasc Imaging 8(1):106–108

    Article  Google Scholar 

  28. Rimoldi O, Schafers KP, Boellaard R et al (2006) Quantification of subendocardial and subepicardial blood flow using 15O-labeled water and PET: experimental validation. J Nucl Med 47(1):163–172

    PubMed  Google Scholar 

  29. Gould KL (2009) Does coronary flow trump coronary anatomy? JACC Cardiovasc Imaging 2(8):1009–1023

    Article  Google Scholar 

  30. Kremers FP, Hofman MB, Groothuis JG et al (2010) Improved correction of spatial inhomogeneities of surface coils in quantitative analysis of first-pass myocardial perfusion imaging. J Magn Reson Imaging 31(1):227–233

    Article  Google Scholar 

  31. Hsu LY, Rhoads KL, Holly JE, Kellman P, Aletras AH, Arai AE (2006) Quantitative myocardial perfusion analysis with a dual-bolus contrast-enhanced first-pass MRI technique in humans. J Magn Reson Imaging 23(3):315–322

    Article  Google Scholar 

  32. Nagel E, Klein C, Paetsch I et al (2003) Magnetic resonance perfusion measurements for the noninvasive detection of coronary artery disease. Circulation 108(4):432–437

    Article  Google Scholar 

  33. Van Beek JH, Roger SA, Bassingthwaighte JB (1989) Regional myocardial flow heterogeneity explained with fractal networks. Am J Physiol 257(5 Pt 2):H1670–H1680

    PubMed  Google Scholar 

  34. Bassingthwaighte JB, Beard DA, Carlson BE, Dash RK, Vinnakota K (2012) Modeling to link regional myocardial work, metabolism and blood flows. Ann Biomed Eng 40(11):2379–2398

    Article  Google Scholar 

  35. Nielles-Vallespin S, Kellman P, Hsu LY, Arai AE (2015) FLASH proton density imaging for improved surface coil intensity correction in quantitative and semi-quantitative SSFP perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson 17:16

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Hsu LY, Kellman P, Arai AE (2008) Nonlinear myocardial signal intensity correction improves quantification of contrast-enhanced first-pass MR perfusion in humans. J Magn Reson Imaging 27(4):793–801

    Article  Google Scholar 

  38. Stegehuis VE, Wijntjens GW, Murai T, Piek JJ, van de Hoef TP (2018) Assessing the haemodynamic impact of coronary artery stenoses: intracoronary flow versus pressure measurements. Eur Cardiol 13(1):46–53

    Article  Google Scholar 

  39. Dietrich O, Reiser MF, Schoenberg SO (2008) Artifacts in 3-T MRI: physical background and reduction strategies. Eur J Radiol 65(1):29–35

    Article  Google Scholar 

  40. Bravo PE, Di Carli MF, Dorbala S (2017) Role of PET to evaluate coronary microvascular dysfunction in non-ischemic cardiomyopathies. Heart Fail Rev 22(4):455–464

    Article  Google Scholar 

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Funding

This study was funded by the Robert Bosch Stiftung and the Berthold Leibinger Stiftung.

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Author notes

  1. Peter Ong and Heiko Mahrholdt have contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Robert-Bosch-Krankenhaus, Auerbachstr. 110, 70376, Stuttgart, Germany

    Andreas Seitz, Giancarlo Pirozzolo, Udo Sechtem, Raffi Bekeredjian, Peter Ong & Heiko Mahrholdt

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  1. Andreas Seitz
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  2. Giancarlo Pirozzolo
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Correspondence to Andreas Seitz.

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Approval from the local ethics committee was obtained and all data acquired in this study were handled anonymously.

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Informed consent was waived for retrospective review of existing patient data (Group 1). Prospectively enrolled study participants (Group 2) gave written informed consent to research participation.

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Seitz, A., Pirozzolo, G., Sechtem, U. et al. Impact of baseline calibration on semiquantitative assessment of myocardial perfusion reserve by adenosine stress MRI. Int J Cardiovasc Imaging 36, 521–532 (2020). https://doi.org/10.1007/s10554-019-01729-z

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  • DOI: https://doi.org/10.1007/s10554-019-01729-z

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