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一种用于定量分析急性冠状动脉闭塞患者心肌灌注 SPECT 中风险心肌的自动方法。

An automatic method for quantification of myocardium at risk from myocardial perfusion SPECT in patients with acute coronary occlusion.

机构信息

Department of Clinical Physiology, Skåne University Hospital, Lund University, 221 85 Lund, Sweden.

出版信息

J Nucl Cardiol. 2010 Oct;17(5):831-40. doi: 10.1007/s12350-010-9237-z. Epub 2010 May 4.

Abstract

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.

摘要

背景

为了确定心肌挽救程度,需要准确量化存在风险的心肌(MaR)。我们提出了一种经过验证的新型自动分割算法,用于定量分析急性冠状动脉闭塞患者的心肌灌注 SPECT(MPS)中的 MaR。

方法和结果

29 例冠状动脉闭塞患者在再灌注前注射灌注示踪剂,并在 4 小时内进行静息 MPS。MaR 使用所提出的算法(Segment 软件)、定量灌注 SPECT(QPS)软件和手动分割进行量化。Segment MaR 算法使用 55%最大计数的阈值和基于正常冠状动脉灌注区域的先验模型。手动分割的 MaR 为 30±10%左心室质量(%LVM),Segment 为 31±12%LVM,QPS 为 36±14%LVM。两种算法的自动分割与手动分割之间具有良好的一致性,与 QPS(5.8±5.8%LVM)相比,Segment 的偏差更小(.8±4.0%LVM)。

结论

在无法获得示踪剂特异性正常数据库的情况下,Segment MaR 算法可用于正确评估急性冠状动脉闭塞患者的 MPS 图像中的 MaR。MaR 与最终梗死面积的关系可以确定心肌挽救程度。

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