Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...

Imaging Studies for Cardiovascular System II:Types of Echocardiography

Imaging Studies for Cardiovascular System II:Types of Echocardiography

Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...

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:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Left Atrial Strain, Amy-Lyon, and Columbia Scores to Predict Atrial Fibrillation in Cardiac Amyloidosis.

JACC. Advances·2026

Same author

Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation.

IEEE transactions on medical imaging·2026

Same author

Heart heard in motion.

The Journal of the Acoustical Society of America·2026

Same author

Echocardiography, Bile Acids and Spleen Stiffness to Rule Out High-Risk Varices in Children With Biliary Atresia.

Liver international : official journal of the International Association for the Study of the Liver·2026

Same author

Microbial sensing through the non-canonical inflammasome modulates airway type 2 immunity.

Frontiers in immunology·2026

Same author

Fourier-plane spatial frequency modulation imaging: enhanced resolution imaging with a single, ultrashort pulse.

Applied optics·2026

Same journal

Theoretical Foundations of the Echo Envelope Statistical Modeling: A Tutorial.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025

Same journal

Practical Demonstrations of FR3-Band Thin-Film Lithium Niobate Acoustic Filter Design.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025

Same journal

Real-Time Heterogeneous Helical Wave Spectrum Method for Transabdominal Passive Acoustic Mapping.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025

Same journal

Cascaded Plane Wave Ultrasound Velocity Vector Imaging: In Vivo Feasibility in Carotid Arteries.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025

Same journal

Quantitative Acoustic Attenuation Scanning Using a Phase-Insensitive Ultrasound Computed Tomography System.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025

Same journal

FPGA-Accelerated CNN Reconstruction for Low-Power Sparse-Array Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 25, 2025

Murine Echocardiography and Ultrasound Imaging

Murine Echocardiography and Ultrasound Imaging

Published on: August 8, 2010

Phase Unwrapping of Color Doppler Echocardiography Using Deep Learning.

Hang Jung Ling, Olivier Bernard, Nicolas Ducros

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|June 26, 2023

Summary

This summary is machine-generated.

We developed a deep learning method to remove aliasing artifacts in color Doppler echocardiography images. The nnU-Net model achieved the best results, showing deep learning

More Related Videos

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Ultrasound-based Pulse Wave Velocity Evaluation in Mice

Ultrasound-based Pulse Wave Velocity Evaluation in Mice

Published on: February 14, 2017

Related Experiment Videos

Last Updated: Jul 25, 2025

Murine Echocardiography and Ultrasound Imaging

Murine Echocardiography and Ultrasound Imaging

Published on: August 8, 2010

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Ultrasound-based Pulse Wave Velocity Evaluation in Mice

Ultrasound-based Pulse Wave Velocity Evaluation in Mice

Published on: February 14, 2017

Area of Science:

Medical Imaging
Cardiovascular Ultrasound
Artificial Intelligence in Medicine

Background:

Color Doppler echocardiography is crucial for assessing intracardiac blood flow.
Aliasing artifacts, or phase wrapping, degrade image quality in specific views.
Accurate quantitative analysis requires correction of these artifacts.

Purpose of the Study:

To develop and evaluate a deep learning model for dealiasing color Doppler echocardiographic images.
To compare the performance of the developed model against state-of-the-art segmentation approaches.
To assess the potential of deep unfolding methods for artifact correction.

Main Methods:

Development of an unfolded primal-dual network (PDNet) for image dealiasing.
Training and evaluation on an in-house dataset of color Doppler echocardiograms.
Comparison with nnU-Net and transformer-based segmentation models, and a semiautomatic technique (DeAN).

Main Results:

The nnU-Net-based method yielded the best dealiased results.
The PDNet demonstrated competitive performance with fewer trainable parameters.
Deep learning methods outperformed the semiautomatic DeAN technique in artifact removal.

Conclusions:

Deep learning-based methods are effective for removing aliasing artifacts in color Doppler echocardiography.
These methods show significant potential for preprocessing images for quantitative analysis.
The PDNet highlights the promise of deep unfolding for medical image enhancement.