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使用钆延迟增强心血管磁共振成像对九种梗死灶定量算法进行体外验证。

Ex-vivo validation of nine algorithms for quantifying infarcts with late gadolinium enhancement cardiovascular magnetic resonance.

作者信息

Kopic Sascha, Heiberg Einar, Engblom Henrik, Carlsson Marcus, Nordlund David, Jablonowski Robert, Kanski Mikael, Xanthis Christos, Bidhult Sebastian, Aletras Anthony H, Arheden Håkan

机构信息

Lund University, Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund, Sweden.

Lund University, Department of Clinical Sciences Lund, Clinical Physiology, Skane University Hospital, Lund, Sweden; Lund University, Wallenberg Center for Molecular Medicine, Lund, Sweden.

出版信息

J Cardiovasc Magn Reson. 2025 May 29;27(2):101915. doi: 10.1016/j.jocmr.2025.101915.

Abstract

BACKGROUND

In cardiovascular magnetic resonance, late gadolinium enhancement (LGE) is the standard method to visualize myocardial infarction (MI). Many algorithms quantifying infarct size in LGE images exist. However, only few algorithms have been validated, i.e., benchmarked against an ex-vivo measurement. Furthermore, the reported algorithm performance varies considerably between studies.

OBJECTIVES

The aim of this study was to compare the performance of all infarct measurement algorithms against an ex-vivo measurement and to promote a discourse regarding advantages and disadvantages of individual measurement methods.

METHODS

MI was induced in 22 pigs. In-vivo LGE imaging was conducted on d0, d3 or d7 post-MI. For ex-vivo validation infarct was measured using high-resolution T1-weighted images. In-vivo infarct size was measured using the full-width at half-maximum (FWHM), n-SD from remote (2,3,5, and 6 SD), feature analysis and combined thresholding (FACT), expectation maximization-weighted A priori information (EWA), Heiberg-08 and Otsu algorithms and manual delineation. No manual adjustments were made to algorithm delineations.

RESULTS

Clear differences in variance and bias were observed between algorithm-based methods, and no method performed optimally in this heterogeneous dataset where the best had a bias of -0.48±3.1, -0.3±4.4%, 2.3±4.2% left ventricle for EWA, FWHM, and FACT, respectively. Manual delineation by experienced observers performed well with a bias of 1.9±5.4%.

CONCLUSION

EWA, Heiberg-08, FWHM, and FACT all perform on par with manual delineation, however, Heiberg-08, and FWHM are not suitable for phase sensitive inversion recovery images. The technique used to measure infarct size should be disclosed in clinical trials and in original research. Caution should be applied when comparing datasets employing different infarct quantification methods. Manual infarct delineation by experienced readers remains a reliable technique to measure infarct size.

摘要

背景

在心血管磁共振成像中,延迟钆增强(LGE)是可视化心肌梗死(MI)的标准方法。存在许多用于量化LGE图像中梗死面积的算法。然而,只有少数算法经过了验证,即与离体测量进行了对比。此外,不同研究报告的算法性能差异很大。

目的

本研究的目的是将所有梗死测量算法的性能与离体测量进行比较,并促进关于个体测量方法优缺点的讨论。

方法

对22头猪诱导心肌梗死。在心肌梗死后第0、3或7天进行体内LGE成像。为了进行离体验证,使用高分辨率T1加权图像测量梗死面积。使用半高宽(FWHM)、距远隔心肌的标准差倍数(2、3、5和6个标准差)、特征分析和联合阈值法(FACT)、期望最大化加权先验信息(EWA)、海伯格-08算法和大津算法以及手动勾勒来测量体内梗死面积。未对算法勾勒进行手动调整。

结果

基于算法的方法之间在方差和偏差方面存在明显差异,在这个异质性数据集中没有一种方法表现最佳,其中EWA、FWHM和FACT的最佳偏差分别为左心室的-0.48±3.1、-0.3±4.4%、2.3±4.2%。经验丰富的观察者进行的手动勾勒表现良好,偏差为1.9±5.4%。

结论

EWA、海伯格-08、FWHM和FACT的性能均与手动勾勒相当,然而,海伯格-08和FWHM不适用于相位敏感反转恢复图像。在临床试验和原始研究中应披露用于测量梗死面积的技术。在比较采用不同梗死量化方法的数据集时应谨慎。经验丰富的读者进行的手动梗死勾勒仍然是测量梗死面积的可靠技术。

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