Accuracy of ventricular volume and ejection fraction measured by gated myocardial SPECT: comparison of 4 software programs.

UNLABELLED Gated myocardial perfusion SPECT has been used to calculate ejection fraction (EF) and end-diastolic volume (EDV) and has correlated well with conventional methods. However, the comparative accuracy of and correlations across various types of gated SPECT software are not well understood. METHODS Mathematic phantoms of cylindric-hemispheric hybrid models, ranging in volume from 34 to 266 mL, were generated. The clinical cases consisted of 30 patients who participated in a radionuclide angiography and gated blood-pool (GBP) study in addition to undergoing (99m)Tc-sestamibi gated SPECT. Four kinds of software, Quantitative Gated SPECT (QGS), the Emory Cardiac Toolbox (ECT), 4D-MSPECT, and Perfusion and Functional Analysis for Gated SPECT (pFAST) were used to compute EF and EDV, and the results were analyzed by multiple comparisons tests. Patients were classified into 4 groups (i.e., no defect, small defect, large defect, and small heart) so that factors affecting variation could be analyzed. RESULTS In mathematic models > or = 74 mL, volume error was within +/-15%, whereas for a small volume (34 mL), QGS and 4D-MSPECT underestimated the volume and pFAST overestimated it. The respective intra- and interobserver reproducibility of the results was good for QGS (r = 0.99 and 1.00), ECT (r = 0.98 and 0.98), and 4D-MSPECT (r = 0.98 and 0.98) and fair for pFAST (r = 0.88 and 0.85). The correlation coefficient for EF between gated SPECT and the GBP study was 0.82, 0.78, 0.69, and 0.84 for QGS, ECT, 4D-MSPECT, and pFAST, respectively. The correlation coefficient for EDV between gated SPECT and the GBP study was 0.88, 0.89, 0.85, and 0.90, respectively. Although good correlation was observed among the 4 software packages, QGS, ECT, and 4D-MSPECT overestimated EF in patients with small hearts, and pFAST overestimated the true volume in patients with large perfusion defects. Correlation coefficients among the 4 kinds of software were 0.80-0.95 for EF and 0.89-0.98 for EDV. CONCLUSION All 4 software programs showed good correlation between EF or EDV and the GBP study. Good correlation was observed also between each pair of quantification methods. However, because each method has unique characteristics that depend on its specific algorithm and thus behaves differently in the various patient subgroups, the methods should not be used interchangeably.