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Grigorios M Karageorgos¹, Pengcheng Liang¹, Nima Mobadersany²

¹Biomedical Engineering Department, Columbia University, New York, NY, United States of America.

Physics in medicine and biology

|June 22, 2023

概括

一种新的深度学习方法增强了基于超声波的动脉壁位移估计,以更好地评估心血管健康. 这种技术提高了绘制动脉硬度和脉冲波速度的准确性.

科学领域:

生物医学工程生物医学工程
医疗成像医学成像
心血管研究研究心血管研究

背景情况:

动脉壁硬度是心血管健康的关键指标.
超声波弹性成像为评估动脉硬性提供了一种非侵入性方法.
精确的动脉壁位移估计对于这些成像技术至关重要.

研究的目的:

开发一种无监督的深度学习方法,以改进动脉壁位移估计.
为了提高动脉硬度和脉冲波速度 (PWV) 映射的质量.
为了验证该方法使用幻影实验和体内体验的人类动脉.

主要方法:

采用了一种无监督的深度学习模型,该模型是从图像注册技术中改造出来的.
模型使用超声波射频信号和B模式图像进行训练,具有不同的损失函数,包括平均平方误差 (MSE).
基于信号与噪声比 (SNR),对比与噪声比 (CNR) 和脉冲波速度 (PWV) 的准确性来评估性能.

主要成果:

在MSE的B模式图像上的训练产生了最高的SNR (30.36 ± 1.14 dB).
使用MSE对射频信号的训练导致最高的CNR (32.84±1.89dB).
经过射频训练的模型准确地绘制了PWV,其平均相对误差 (MRE_PWV) 在幻体中为3.32 ± 1.80%,在体内为3.86 ± 2.69%,R值分别为0.97和0.95.

结论:

提出的深度学习方法显著改善了动脉壁位移估计.
该方法在幻影研究和体内研究中都显示出高准确性.
这种技术有望推进血管弹性成像和心血管评估.

关键词:

动脉壁的移位动脉壁的移位冠状动脉疾病冠状动脉疾病深度学习是一种深度学习.神经网络的神经网络的神经网络血管弹性成像血管弹性成像

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