Intra- and inter-operator repeatability of myocardial blood flow and myocardial flow reserve measurements using rubidium-82 pet and a highly automated analysis program
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Changes in myocardial blood flow between rest and stress states are commonly used to diagnose coronary artery disease. Relative myocardial perfusion imaging (MPI) is used routinely while myocardial blood flow quantification (MBF) may improve the sensitivity for detection of early disease. The ratio of flow at stress and rest (S/R) and their difference (S-R) have both been proposed as a means to detect regions with reduced myocardial flow reserve (MFR). In this study, we describe a highly automated method to calculate regional and global rest, stress, S/R, and S-R polar maps of the left ventricle myocardium.
We measured the inter- and intra-operator variability using two randomized datasets (n = 30 each) for each of two operators (novice and expert) with correlation and Bland-Altman reproducibility coefficient (RPC%) analyses.
S-R MBF had less inter-operator dependent variability than S/R (RPC% = 5.0% vs 12.6%, P < .001). While there was no difference in intra-operator variability with S-R MBF (novice vs expert RPC% = 6.4% vs 5.9%, P = ns), variability was higher in the novice-operator for S/R (RPC% = 16.8% vs 8.5% respectively, P < .001), suggesting that S-R may be preferred for detecting small changes in MFR. The novice operator’s intervention pattern became more similar to that of the expert in the later dataset, emphasizing the need for adequate training and quality assurance.
The proposed method results in low operator-dependent variability, suitable for routine use.
KeywordsPET rubidium-82 image processing coronary blood flow operator repeatability
RK, RSB and RAD are receiving licensing revenues and consultant fees from DraxImage. RK, JMR and RAD are receiving licensing revenues from FlowQuant.
This work is supported by the following: Canadian Institute for Health Research Operating Grants MOP-79311 and MIS-100935, Ontario Research Fund Grant RE-02-038, Heart and Stroke Foundation of Ontario Program Grant # PRG6242, Canadian Foundation for Innovation—Leading Edge Fund Grant# 11306. Ran Klein was supported in part by the Natural Sciences and Engineering Research Council—Canadian Graduate Scholarship, and by the Heart and Stroke Foundation of Ontario—Doctoral Research Award. Maria C. Ziadi is a Research Fellow supported by University of Ottawa International Fellowship Award and, the Molecular Function and Imaging Program (HSFO grant # PRG6242). Stephanie L. Thorn is supported by the Heart and Stroke Foundation of Ontario—Doctoral Scholarship. Andy Adler is supported by the Natural Sciences and Engineering Research Council. Rob S. Beanlands is a Career Investigator supported by the Heart and Stroke Foundation of Ontario.
- 10.Yoshinaga K, Tamaki N, Ruddy TD, deKemp RA, Beanlands RSB. Evaluation of myocardial perfusion. In: Wahl RL, editor. Principles and practice of PET and PET/CT, 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2009. p. 541-64.Google Scholar
- 11.Schindler TH, Nitzsche EU, Schelbert HR, Olschewski M, Sayre J, Mix M, et al. Positron emission tomography-measured abnormal responses of myocardial blood flow to sympathetic stimulation are associated with the risk of developing cardiovascular events. J Am Coll Cardiol 2005;45:1505-12.CrossRefPubMedGoogle Scholar
- 17.deKemp RA, Klein R, Renaud JM, Alghamdi A, Lortie M, DaSilva J, et al. 3D listmode cardiac PET for simultaneous quantification of myocardial blood flow and ventricular function. IEEE NSS-MIC Conference Record 2008:5215-8.Google Scholar
- 23.deKemp R, Klein R, Lortie M, Beanlands R. Constant-activity-rate infusions for myocardial blood flow quantification with 82Rb and 3D PET. IEEE NSS-MIC Conference Record 2006;6:3519-21.Google Scholar
- 24.Lammertsma AA. Myocardial perfusion in 3 dimensions. J Nucl Med 2002;48:1041-3.Google Scholar
- 29.DeGrado TR, Hanson MW, Turkington TG, Delong DM, Brezinski DA, Vallée JP, et al. Estimation of myocardial blood flow for longitudinal studies with 13 N-labeled ammonia and positron emission tomography. J Nucl Med 1996;3:494-507.Google Scholar
- 32.Scott NS, Le May MR, deKemp RA, Ruddy TD, Labinaz M, Marquis JF, et al. Evaluation of myocardial perfusion using rubidium-82 positron emission tomography after myocardial infarction in patients receiving primary stent implantation or thrombolytic therapy. Am J Cardiol 2001;88:886-9.CrossRefPubMedGoogle Scholar
- 33.Sawada S, Muzik O, Beanlands RS, Wolfe E, Hutchins GD, Schwaiger M. Interobserver and interstudy variability of myocardial blood flow and flow-reserve measurements with nitrogen 13 ammonia-labeled positron emission tomography. J Nucl Med 1995;2:413-22.Google Scholar
- 44.PMOD Technologies (Online). http://www.pmod.com.
- 46.Dilsizian V, Bacharach SL, Beanlands RS, Bergmann SR, Delbeke D, Gropler RJ, et al. 2009, July. http://www.asnc.org.
- 47.Gerwitz H, Fischman AJ, Abraham S, Gilson M, Strauss HW, Alpert NM. Positron emission tomographic measurements of absolute regional myocardial blood flow permits identification of nonviable myocardium in patients with chronic myocardial infarction. J Am Coll Cardiol 1994;23:851-9.CrossRefGoogle Scholar
- 50.Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician 1983:307-17.Google Scholar
- 52.Chareonthaitawee P, Kaufmann PA, Rimoldi O, Camici PG. Heterogeneity of resting and hyperemic myocardial blood flow in healthy humans. Circ Res 2001;50:151-61.Google Scholar