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Machine learning-enabled quantitative ultrasound techniques for tissue differentiation

Thomson, Hannah; Yang, Shufan; Cochran, Sandy

Authors

Hannah Thomson

Sandy Cochran



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. Conclusion: We were able to generate classification parametric images in near-real-time speed as a potential diagnostic tool in the operating room for the possible use for human brain tissue characterisation.

Journal Article Type Article
Acceptance Date Apr 18, 2022
Online Publication Date Jul 15, 2022
Publication Date 2022-10
Deposit Date Apr 20, 2022
Publicly Available Date Apr 20, 2022
Print ISSN 1346-4523
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 49
Pages 517-528
DOI https://doi.org/10.1007/s10396-022-01230-6
Keywords Quantitative ultrasound, Ultrasound phantoms, Tissue characterisation, Parametric imaging, Binary classifier, Machine learning
Public URL http://researchrepository.napier.ac.uk/Output/2865555

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