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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...

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Related Experiment Video

Updated: May 30, 2025

Ultrasonic Assessment of Myocardial Microstructure

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

Regional Image Quality Scoring for 2-D Echocardiography Using Deep Learning.

Gilles Van De Vyver¹, Svein-Erik Måsøy¹, Håvard Dalen²

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway.

Ultrasound in Medicine & Biology

|January 26, 2025

Summary

This summary is machine-generated.

A deep learning model accurately estimates echocardiography image quality, outperforming traditional metrics. This tool offers a generalizable solution for objective ultrasound assessment.

Keywords:

Cardiac segmentation Coherence Image quality Signal-to-noise ratio Ultrasound

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Related Experiment Videos

Last Updated: May 30, 2025

Ultrasonic Assessment of Myocardial Microstructure

Ultrasonic Assessment of Myocardial Microstructure

Published on: January 14, 2014

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

Murine Echocardiography and Ultrasound Imaging

Murine Echocardiography and Ultrasound Imaging

Published on: August 8, 2010

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Echocardiography is crucial for cardiac assessment, but image quality can be subjective.
Objective and automated methods for assessing regional ultrasound image quality are needed.

Purpose of the Study:

To develop and compare automated methods for estimating regional echocardiography image quality.
To evaluate these methods against expert manual annotations.

Main Methods:

Three methods were developed: generalized contrast-to-noise ratio (gCNR), local image coherence, and a deep convolutional network (CNN).
A U-Net model was used for segmentation in the gCNR method.
The CNN model directly predicted regional image quality.

Main Results:

The CNN achieved the highest correlation with expert annotations (ρ = 0.69), comparable to inter-observer variability (ρ = 0.63).
The coherence-based method showed good performance (ρ = 0.58) and greater generalizability.
The gCNR metric demonstrated poor performance (ρ = 0.24).

Conclusions:

Deep convolutional networks offer the most accurate regional image quality prediction for echocardiography.
The coherence-based method provides a more generalizable approach to automated quality assessment.
An open-source Python library (arqee) is available for image quality prediction.