Thomson, H., Yang, S. and Cochran, S. (2022) Machine learning-enabled quantitative ultrasound techniques for tissue differentiation. Journal of Medical Ultrasonics, 49(4), pp. 517-528. (doi: 10.1007/s10396-022-01230-6)
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Abstract
Purpose: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for tissue differentiation. Methods: This study first validated chicken liver and gizzard muscle as suitable acoustic phantoms for human brain and brain tumour tissues via measurement of the speed of sound and acoustic attenuation. A total of thirteen QUS parameters were estimated from twelve samples, each using data obtained with a transducer with a frequency of 5–11 MHz. Spectral parameters, i.e., effective scatterer diameter and acoustic concentration, were calculated from the backscattered power spectrum of the tissue, and echo envelope statistics were estimated by modelling the scattering inside the tissue as a homodyned K-distribution, yielding the scatterer clustering parameter α and the structure parameter κ. Standard deviation and higher-order moments were calculated from the echogenicity value assigned in conventional B-mode images. Results: The k-nearest neighbours algorithm was used to combine those parameters, which achieved 94.5% accuracy and 0.933 F1-score.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Thomson, Miss Hannah and Cochran, Professor Sandy and Yang, Dr Shufan |
| Authors: | Thomson, H., Yang, S., and Cochran, S. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Journal Name: | Journal of Medical Ultrasonics |
| Publisher: | Springer |
| ISSN: | 1346-4523 |
| ISSN (Online): | 1613-2254 |
| Published Online: | 15 July 2022 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in Journal of Medical Ultrasonics 49(4): 517-528 |
| Publisher Policy: | Reproduced under a Creative Commons licence |
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