Normal values of high-resolution transmural perfusion distribution metrics for automated quantitative pixel-wise myocardial perfusion cardiovascular magnetic resonance.

IF 6.1 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Christel H Kamani, Louise Brown, Thomas Anderton, Raluca Tomoaia, Chin Soo, Gaurav S Gulsin, David A Broadbent, Jian L Yeo, Alice L Wood, Christopher E D Saunderson, Ioannis Botis, Arka Das, Nicholas Jex, Amrit Chowdhary, Sharmaine Thirunavukarasu, Noor Sharrack, Peter P Swoboda, Hui Xue, John P Greenwood, David Adlam, Eylem Levelt, Gerry P McCann, Peter Kellman, Sven Plein
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引用次数: 0

Abstract

Background: The myocardial blood flow (MBF) transmural distribution between the subendocardial (ENDO) and subepicardial (EPI) layers under resting and hyperemic conditions can aid in the diagnosis of several forms of heart disease. Recently proposed automated in-line myocardial perfusion cardiovascular magnetic resonance (CMR) allows pixel-wise quantification of ENDO- and EPI-MBF, but normal values for these parameters are lacking. We therefore aimed to establish normal values for transmural distribution of MBF in a healthy population.

Methods: 138 healthy participants from two centers underwent adenosine stress and rest myocardial perfusion CMR. Global and myocardial slice-specific stress/rest ENDO- and EPI-MBF values were derived using pixel-wise in-line automatic post-processing, and transmural perfusion metrics (ENDO and EPI myocardial perfusion reserve [MPRENDO, MPREPI]; stress and rest ENDO-to-EPI gradient [sGRAD and rGRAD]) were computed using the Gadgetron software.

Results: The study cohort comprised 84 males and 54 females (mean age: 50±36) with no cardiovascular disease or risk factors. In the entire cohort, MPRENDO (3.3±1.2) was significantly lower (p<0.001) than MPREPI (3.9±1.2). sGRAD (0.98±0.09) was significantly lower (p<0.001) than rGRAD (1.11±0.07). "While there were no sex-specific differences in the majority of these metrics, all correlated inversely with increasing age. We propose specific values for each slice. These are conditional to the pulse sequence, acquisition timing and analysis method used in this work, as mean ± SD values at the basal, mid and apical level for MPRENDO (3.7±1.1, 3.3±0.9, 3.6±1.0), MPREPI (4.0±1.1, 3.9±1.1, 4.0±1.1), sGRAD (1.00±0.13, 0.92±0.09, 1.06±0.18) and rGRAD (1.10±0.09, 1.09±0.07, 1.18±0.11).

Conclusion: Normal global and myocardial slice-specific values of MPRENDO, MPREPI, sGRAD and rGRAD using in-line automated MBF quantification from first pass myocardial perfusion CMR are presented. While there were no sex-specific differences in any of these metrics, all correlated inversely with increasing age. Understanding the MBF dynamics of the myocardial layers in healthy subjects will help to characterize MBF alterations in patients with coronary artery disease or microvascular dysfunction.

自动定量逐像素心肌灌注CMR高分辨率跨壁灌注分布指标的正常值。
背景:静息和充血状态下心肌血流量(MBF)在心内膜下(ENDO)和心外膜下(EPI)层之间的跨壁分布有助于多种心脏病的诊断。最近提出的自动在线心肌灌注心血管磁共振(CMR)允许逐像素量化ENDO-和EPI-MBF,但缺乏这些参数的正常值。目的:建立健康人群中MBF跨壁分布的正常值。方法:来自两个中心的138名健康受试者进行了腺苷应激和静息心肌灌注CMR。使用逐像素的在线自动后处理和跨壁灌注指标[ENDO和EPI心肌灌注储备(MPRENDO, MPREPI)]获得全局和心肌切片特异性应力/休息ENDO-和EPI- mbf值;应力和静息梯度(sGRAD和rGRAD)]用Gadgetron软件计算。结果:研究队列包括84名男性和54名女性(平均年龄:50±36),无心血管疾病或危险因素。在整个队列中,MPRENDO(3.3±1.2)显著低于pEPI(3.9±1.2)。sGRAD(0.98±0.09)显著低于pENDO(3.7±1.1,3.3±0.9,3.6±1.0)、MPREPI(4.0±1.1,3.9±1.1,4.0±1.1)、sGRAD(1.00±0.13,0.92±0.09,1.06±0.18)和rGRAD(1.10±0.09,1.09±0.07,1.18±0.11)。结论:通过首次心肌灌注CMR的在线自动MBF定量,我们获得了MPRENDO、MPREPI、sGRAD和rGRAD的正常全局值和心肌切片特异性值。虽然这些指标都没有性别差异,但都与年龄增长呈负相关。了解健康受试者心肌层的MBF动态将有助于表征冠状动脉疾病或微血管功能障碍患者的MBF改变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.90
自引率
12.50%
发文量
61
审稿时长
6-12 weeks
期刊介绍: Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to: New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system. New methods to enhance or accelerate image acquisition and data analysis. Results of multicenter, or larger single-center studies that provide insight into the utility of CMR. Basic biological perceptions derived by CMR methods.
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