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弥合测量与建模之间的差距:心血管功能替身。

Bridging the gap between measurements and modelling: a cardiovascular functional avatar.

机构信息

Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.

Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

出版信息

Sci Rep. 2017 Jul 24;7(1):6214. doi: 10.1038/s41598-017-06339-0.

Abstract

Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements.

摘要

集中参数模型在心血管系统中具有帮助研究人员和临床医生更好地理解心血管功能的潜力。当这些模型具有个体特异性时,它们的价值会增加。然而,迄今为止,大多数针对集中参数模型的个性化方法都需要进行侵入性测量,或者由于缺乏必要的临床数据而无法实现个体特异性。在这里,我们提出了一种仅使用非侵入性测量来个性化心脏和全身循环模型参数的方法。个性化模型是使用四维磁共振成像的流量数据和肱动脉的袖带压力测量值创建的。我们将这种个性化模型称为心血管化身。在我们的概念验证研究中,我们评估了化身在一组 8 名健康受试者中重现压力和流量的能力。无论是定量还是定性,基于模型的结果都与每个受试者体内获得的压力和流量测量值非常吻合。这种非侵入性和个性化的方法可以将医学数据综合为心血管功能的临床相关指标,并估计不能直接从临床测量中评估的血液动力学变量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f19b/5524911/b926d3684453/41598_2017_6339_Fig1_HTML.jpg

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