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Updated: May 13, 2026

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

通过深度学习提高心脏颜色多普勒率

Julia Puig, Denis Friboulet, Hang Jung Ling

IEEE transactions on ultrasonics, ferroelectrics, and frequency control

|July 8, 2024

概括

包括ConvNeXt在内的深度学习模型,从有限的心声回声学数据中增强心内多普勒速度估计. 这些方法提高了准确性和稳定性,优于传统技术,用于更好的心脏分析.

科学领域:

医疗成像医学成像
人工智能的人工智能
心血管生理学心血管生理学

背景情况:

彩色多普勒回声心电图可视化心脏血液流动.
有限的率阻碍了定量速度评估和心室填充分析.
深度学习显示了回声心电图数据后处理的潜力.

研究的目的:

研究用于心内多普勒速度估计的深度学习模型.
用减少过的I/Q信号来评估速度估计.
为了比较U-Net和ConvNeXt架构和真实与复杂数据表示.

主要方法:

监督学习与模拟的心脏颜色多普勒采集.
数据增强策略,以扩大培训数据集.
已经实现了卷积神经网络 (CNN) 架构 (U-Net,ConvNeXt).

主要成果:

无论是U-Net还是ConvNeXt模型,在缓解别名和噪声方面都超过了自动校正器方法.
在真实值和复杂值数据之间没有观察到显著的性能差异.
ConvNeXt在体内别名样本上独特地提供了高质量的结果.

结论:

监督深度学习方法是从有限的心声回声学收购对多普勒速度估计有希望的.
这些模型显示出对噪声的稳定性和与基线相比的可比量化结果.
ConvNeXt在挑战体内数据方面表现出特别高的效率.

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Assessing Blood pressure using a doppler ultrasound

Assessing Blood pressure using a doppler ultrasound

To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment: