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This work was to determine threshold values for accurate measurements of left ventricular end-diastolic volume(EDV),end-systolic volume(ESV),and ejection fraction(EF) from electrocardiography-gated myocardial perfusion imaging(MPI) in Chinese,and these data were compared with those of echocardiography.A total of 110 patients with definite or suspected coronary artery disease were referred for both gated MPI and echocardiography within 1 week.The EDV,ESV,and EF automatically measured by MyoMetrix and echocardiography were analyzed using Bland-Altman plot correlation and paired t test.The results showed that these parameters quantified by MyoMetrix software were correlated,moderately to highly,with those on echocardiography(ρ,r ≥0.75,P<0.01).However,the EF was not significantly correlated,with post-exercise MPI ESV of <15 mL or resting MPI ESV of <20 mL.At or above this ESV value,EF was underestimated by MyoMetrix(t≥ 4.60,P<0.01).In a word,a small ESV was underestimated by MyoMetrix,which could lead to EF overestimation.On the contrary,a normal or large ESV was overestimated by MyoMetrix,which led to EF underestimation.
This work was to determine threshold values for accurate measurements of left ventricular end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) from electrocardiography-gated myocardial perfusion imaging (MPI) data were compared with those of echocardiography. A total of 110 patients with definite or suspected coronary artery disease were referred for both gated MPI and echocardiography within 1 week. EDV, ESV, and EF automatically measured by MyoMetrix and echocardiography were analyzed using Bland- Altman plot correlation and paired t test. The results showed that these parameters quantified by MyoMetrix software were correlated, moderately to highly, with those on echocardiography (ρ, r ≥ 0.75, P <0.01) .However, the EF was not significantly correlated, with post-exercise MPI ESV of <15 mL or resting MPI ESV of <20 mL. At or above this ESV value, EF was underestimated by MyoMetrix (t ≥ 4.60, P <0.01) .In a word, a small ESV was underestimated by MyoMetrix , which could lead to EF overestimation. On the contrary, a normal or large ESV was overestimated by MyoMetrix, which led to EF underestimation.