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在活体中量化动脉活力力学,使用深度学习辅助的压力区域分析

Yuxuan Jiang¹, Yanping Cao²

¹Institute of Biomechanics and Medical Engineering, AML, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China.

Biomechanics and modeling in mechanobiology

|March 5, 2026

概括

这项研究引入了一种测量活跃动脉机制的新方法,揭示了血管光滑肌活动在运动后保持高于血压的时间,为生理应激反应提供了洞察力.

科学领域:

心血管生理学心血管生理学
生物医学工程生物医学工程
医疗成像医学成像

背景情况:

由血管光滑肌驱动的活跃动脉机制对于心血管健康和疾病至关重要.
目前的方法很难分离平滑肌肉对动脉硬性的贡献.
积极动脉性质的定量评估具有挑战性.

研究的目的:

开发和验证一种用于量化活性动脉机制的新型框架.
为了研究运动后活跃动脉机制的恢复.
为了探索恢复期间血压和平滑肌肉度之间的关系.

主要方法:

开发了一个压力区域分析框架,整合了超声波,血压和神经网络.
利用基于神经网络的细分来对动脉横截面积进行分析.
采用生物力学模型驱动的反转来推断活性机械性质.
招募233名志愿者参加网络培训,10名志愿者参加运动测试.

主要成果:

神经网络在细分动脉区域方面表现强.
活动动脉机制在运动后的15分钟内保持升高,比静脉压 (~5分钟) 长.
观察到血压和光滑肌肉恢复之间的分离.

结论:

拟议的框架有效量化了活性动脉机制.
运动后恢复显示出血压和血管光滑肌肉活动的独特时间模式.
这些发现为在生理压力下血管光滑肌肉调节提供了新的见解.

关键词:

活动力学活动力学活动力学动脉动脉的动脉是一种动脉.深度学习是一种深度学习.运动后的时间.船舶细分船舶的细分

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