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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

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Updated: Sep 4, 2025

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Published on: March 4, 2015

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

Hannah Thomson¹, Shufan Yang^2,3, Sandy Cochran²

¹Centre for Medical and Industrial Ultrasonics, University of Glasgow, University Avenue, Glasgow, UK. s.yang@napier.ac.uk.

Journal of Medical Ultrasonics (2001)

|July 15, 2022

Summary

This summary is machine-generated.

This study demonstrates quantitative ultrasound (QUS) for differentiating human brain tissues using advanced spectral and statistical parameters. The developed method achieved high accuracy, offering a potential real-time diagnostic tool for operating rooms.

Keywords:

Binary classifier Machine learning Parametric imaging Quantitative ultrasound Tissue characterisation Ultrasound phantoms

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Last Updated: Sep 4, 2025

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Area of Science:

Medical Imaging
Biophysics
Ultrasound Technology

Background:

Quantitative ultrasound (QUS) analyzes backscattered ultrasound data to reveal tissue microstructure.
Implementing practical QUS parameters is crucial for effective tissue differentiation.

Purpose of the Study:

To describe the implementation of practical quantitative ultrasound (QUS) parameters for tissue differentiation.
To validate chicken liver and gizzard muscle as acoustic phantoms for human brain and brain tumor tissues.

Main Methods:

Thirteen QUS parameters were estimated using a 5-11 MHz transducer.
Spectral parameters (effective scatterer diameter, acoustic concentration) and echo envelope statistics (scatterer clustering parameter α, structure parameter κ) were calculated.
The k-nearest neighbors algorithm was employed to combine parameters for classification.

Main Results:

The study achieved 94.5% accuracy and a 0.933 F1-score in tissue classification.
Classification parametric images were generated at near-real-time speeds.

Conclusions:

The developed QUS method shows potential as a diagnostic tool for human brain tissue characterization.
Near-real-time classification parametric imaging could be valuable in the operating room.