BACKGROUND: In order to determine myocardial salvage, accurate quantification of myocardium at risk (MaR) is necessary. We present a validated novel automatic segmentation algorithm for quantification of MaR by myocardial perfusion SPECT (MPS) in patients with acute coronary occlusion. METHODS AND RESULTS: Twenty-nine patients with coronary occlusion were injected with a perfusion tracer before reperfusion, and underwent rest MPS within 4 hours. The MaR was quantified using the proposed algorithm (Segment software), the software Quantitative Perfusion SPECT (QPS) and by manual segmentation. The Segment MaR algorithm used a threshold of 55% of maximal counts and an a priori model based on normal coronary artery perfusion territories. The MaR was 30 ± 10% left ventricular mass (%LVM) by manual segmentation, 31 ± 12%LVM by Segment, and 36 ± 14%LVM by QPS. There was a good agreement between automatic and manual segmentation for both of the algorithms with a lower bias for Segment (.8 ± 4.0%LVM) than for QPS (5.8 ± 5.8%LVM) when compared to manual segmentation. CONCLUSIONS: The Segment MaR algorithm can be used to correctly assess MaR from MPS images in patients with acute coronary occlusion without access to tracer-specific normal database. The MaR in relation to final infarct size enables determination of myocardial salvage.
BACKGROUND: In order to determine myocardial salvage, accurate quantification of myocardium at risk (MaR) is necessary. We present a validated novel automatic segmentation algorithm for quantification of MaR by myocardial perfusion SPECT (MPS) in patients with acute coronary occlusion. METHODS AND RESULTS: Twenty-nine patients with coronary occlusion were injected with a perfusion tracer before reperfusion, and underwent rest MPS within 4 hours. The MaR was quantified using the proposed algorithm (Segment software), the software Quantitative Perfusion SPECT (QPS) and by manual segmentation. The Segment MaR algorithm used a threshold of 55% of maximal counts and an a priori model based on normal coronary artery perfusion territories. The MaR was 30 ± 10% left ventricular mass (%LVM) by manual segmentation, 31 ± 12%LVM by Segment, and 36 ± 14%LVM by QPS. There was a good agreement between automatic and manual segmentation for both of the algorithms with a lower bias for Segment (.8 ± 4.0%LVM) than for QPS (5.8 ± 5.8%LVM) when compared to manual segmentation. CONCLUSIONS: The Segment MaR algorithm can be used to correctly assess MaR from MPS images in patients with acute coronary occlusion without access to tracer-specific normal database. The MaR in relation to final infarct size enables determination of myocardial salvage.
Authors: Manuel D Cerqueira; Neil J Weissman; Vasken Dilsizian; Alice K Jacobs; Sanjiv Kaul; Warren K Laskey; Dudley J Pennell; John A Rumberger; Thomas Ryan; Mario S Verani Journal: Circulation Date: 2002-01-29 Impact factor: 29.690
Authors: K F Van Train; J Areeda; E V Garcia; C D Cooke; J Maddahi; H Kiat; G Germano; G Silagan; R Folks; D S Berman Journal: J Nucl Med Date: 1993-09 Impact factor: 10.057
Authors: S Tamaki; H Nakajima; T Murakami; Y Yui; H Kambara; K Kadota; A Yoshida; C Kawai; N Tamaki; T Mukai; Y Ishii; K Torizuka Journal: Circulation Date: 1982-11 Impact factor: 29.690
Authors: E E DePasquale; A C Nody; E G DePuey; E V Garcia; G Pilcher; C Bredlau; G Roubin; A Gober; A Gruentzig; P D'Amato Journal: Circulation Date: 1988-02 Impact factor: 29.690
Authors: Helen Fransson; Michael Ljungberg; Marcus Carlsson; Henrik Engblom; Håkan Arheden; Einar Heiberg Journal: J Nucl Cardiol Date: 2014-02-15 Impact factor: 5.952
Authors: Daniel R Ludwig; Mati Friehling; Erik B Schelbert; David Schwartzman Journal: Eur J Nucl Med Mol Imaging Date: 2013-11-09 Impact factor: 9.236