PURPOSE: In clinical cardiac (82)Rb PET, globally impaired coronary flow reserve (CFR) is a relevant marker for predicting short-term cardiovascular events. However, there are limited data on the impact of different software and methods for estimation of myocardial blood flow (MBF) and CFR. Our objective was to compare quantitative results obtained from previously validated software tools. METHODS: We retrospectively analyzed cardiac (82)Rb PET/CT data from 25 subjects (group 1, 62 ± 11 years) with low-to-intermediate probability of coronary artery disease (CAD) and 26 patients (group 2, 57 ± 10 years; P=0.07) with known CAD. Resting and vasodilator-stress MBF and CFR were derived using three software applications: (1) Corridor4DM (4DM) based on factor analysis (FA) and kinetic modeling, (2) 4DM based on region-of-interest (ROI) and kinetic modeling, (3) MunichHeart (MH), which uses a simplified ROI-based retention model approach, and (4) FlowQuant (FQ) based on ROI and compartmental modeling with constant distribution volume. RESULTS: Resting and stress MBF values (in milliliters per minute per gram) derived using the different methods were significantly different: using 4DM-FA, 4DM-ROI, FQ, and MH resting MBF values were 1.47 ± 0.59, 1.16 ± 0.51, 0.91 ± 0.39, and 0.90 ± 0.44, respectively (P<0.001), and stress MBF values were 3.05 ± 1.66, 2.26 ± 1.01, 1.90 ± 0.82, and 1.83 ± 0.81, respectively (P<0.001). However, there were no statistically significant differences among the CFR values (2.15 ± 1.08, 2.05 ± 0.83, 2.23 ± 0.89, and 2.21 ± 0.90, respectively; P=0.17). Regional MBF and CFR according to vascular territories showed similar results. Linear correlation coefficient for global CFR varied between 0.71 (MH vs. 4DM-ROI) and 0.90 (FQ vs. 4DM-ROI). Using a cut-off value of 2.0 for abnormal CFR, the agreement among the software programs ranged between 76 % (MH vs. FQ) and 90 % (FQ vs. 4DM-ROI). Interobserver agreement was in general excellent with all software packages. CONCLUSION: Quantitative assessment of resting and stress MBF with (82)Rb PET is dependent on the software and methods used, whereas CFR appears to be more comparable. Follow-up and treatment assessment should be done with the same software and method.
PURPOSE: In clinical cardiac (82)Rb PET, globally impaired coronary flow reserve (CFR) is a relevant marker for predicting short-term cardiovascular events. However, there are limited data on the impact of different software and methods for estimation of myocardial blood flow (MBF) and CFR. Our objective was to compare quantitative results obtained from previously validated software tools. METHODS: We retrospectively analyzed cardiac (82)Rb PET/CT data from 25 subjects (group 1, 62 ± 11 years) with low-to-intermediate probability of coronary artery disease (CAD) and 26 patients (group 2, 57 ± 10 years; P=0.07) with known CAD. Resting and vasodilator-stress MBF and CFR were derived using three software applications: (1) Corridor4DM (4DM) based on factor analysis (FA) and kinetic modeling, (2) 4DM based on region-of-interest (ROI) and kinetic modeling, (3) MunichHeart (MH), which uses a simplified ROI-based retention model approach, and (4) FlowQuant (FQ) based on ROI and compartmental modeling with constant distribution volume. RESULTS: Resting and stress MBF values (in milliliters per minute per gram) derived using the different methods were significantly different: using 4DM-FA, 4DM-ROI, FQ, and MH resting MBF values were 1.47 ± 0.59, 1.16 ± 0.51, 0.91 ± 0.39, and 0.90 ± 0.44, respectively (P<0.001), and stress MBF values were 3.05 ± 1.66, 2.26 ± 1.01, 1.90 ± 0.82, and 1.83 ± 0.81, respectively (P<0.001). However, there were no statistically significant differences among the CFR values (2.15 ± 1.08, 2.05 ± 0.83, 2.23 ± 0.89, and 2.21 ± 0.90, respectively; P=0.17). Regional MBF and CFR according to vascular territories showed similar results. Linear correlation coefficient for global CFR varied between 0.71 (MH vs. 4DM-ROI) and 0.90 (FQ vs. 4DM-ROI). Using a cut-off value of 2.0 for abnormal CFR, the agreement among the software programs ranged between 76 % (MH vs. FQ) and 90 % (FQ vs. 4DM-ROI). Interobserver agreement was in general excellent with all software packages. CONCLUSION: Quantitative assessment of resting and stress MBF with (82)Rb PET is dependent on the software and methods used, whereas CFR appears to be more comparable. Follow-up and treatment assessment should be done with the same software and method.
Authors: Keiichiro Yoshinaga; Benjamin J W Chow; Kathryn Williams; Li Chen; Robert A deKemp; Linda Garrard; Alexander Lok-Tin Szeto; May Aung; Ross A Davies; Terrence D Ruddy; Rob S B Beanlands Journal: J Am Coll Cardiol Date: 2006-08-17 Impact factor: 24.094
Authors: Georges El Fakhri; Arkadiusz Sitek; Bastien Guérin; Marie Foley Kijewski; Marcelo F Di Carli; Stephen C Moore Journal: J Nucl Med Date: 2005-08 Impact factor: 10.057
Authors: Mireille Lortie; Rob S B Beanlands; Keiichiro Yoshinaga; Ran Klein; Jean N Dasilva; Robert A DeKemp Journal: Eur J Nucl Med Mol Imaging Date: 2007-07-07 Impact factor: 9.236
Authors: Timothy M Bateman; Gary V Heller; A Iain McGhie; John D Friedman; James A Case; Jan R Bryngelson; Ginger K Hertenstein; Kelly L Moutray; Kimberly Reid; S James Cullom Journal: J Nucl Cardiol Date: 2006 Jan-Feb Impact factor: 5.952
Authors: Uchechukwu K Sampson; Sharmila Dorbala; Atul Limaye; Raymond Kwong; Marcelo F Di Carli Journal: J Am Coll Cardiol Date: 2007-02-26 Impact factor: 24.094
Authors: Jonathan B Moody; Benjamin C Lee; James R Corbett; Edward P Ficaro; Venkatesh L Murthy Journal: J Nucl Cardiol Date: 2015-04-14 Impact factor: 5.952
Authors: Chad R R N Hunter; Jeremy Hill; M Cecilia Ziadi; Rob S B Beanlands; Robert A deKemp Journal: Eur J Nucl Med Mol Imaging Date: 2015-03-28 Impact factor: 9.236