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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:

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Related Experiment Video

Updated: May 13, 2026

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Boosting Cardiac Color Doppler Frame Rates With Deep Learning.

Julia Puig, Denis Friboulet, Hang Jung Ling

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|July 8, 2024

Summary

This summary is machine-generated.

Deep learning models, including ConvNeXt, enhance intracardiac Doppler velocity estimation from limited echocardiography data. These methods improve accuracy and robustness, outperforming traditional techniques for better cardiac analysis.

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Physiology

Background:

Color Doppler echocardiography visualizes cardiac blood flow.
Limited frame rates hinder quantitative velocity assessment and ventricular filling analysis.
Deep learning shows potential in echocardiographic data postprocessing.

Purpose of the Study:

To investigate deep learning models for intracardiac Doppler velocity estimation.
To assess velocity estimation using reduced filtered I/Q signals.
To compare U-Net and ConvNeXt architectures and real vs. complex data representations.

Main Methods:

Supervised learning with simulated cardiac color Doppler acquisitions.
Data augmentation strategies to expand the training dataset.
Convolutional Neural Network (CNN) architectures (U-Net, ConvNeXt) were implemented.

Main Results:

Both U-Net and ConvNeXt models surpassed the autocorrelator method in mitigating aliasing and noise.
No significant performance difference was observed between real and complex-valued data.
ConvNeXt uniquely provided high-quality results on in vivo aliased samples.

Conclusions:

Supervised deep learning methods are promising for Doppler velocity estimation from limited echocardiographic acquisitions.
The models demonstrated robustness to noise and comparable quantitative results to the baseline.
ConvNeXt shows particular efficacy for challenging in vivo data.