Research Output
Machine Learning Enabled Quantitative Ultrasound Techniques for Tissue Differentiation
  Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered via radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for tissue differentiation. 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 5 - 11 MHz transducer. Spectral parameters - 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. The k-nearest neighbours algorithm was used to combine those parameters which achieved 94.5% accuracy and 0.933 F1-score. We are able to generate classification parametric images with near-real-time speed as a potential diagnostic tool in the operating room for the possible use for human brain tissue characterisation.

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  • Date:

    15 July 2022

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  • Funders:

    Royal Society of Edinburgh; Engineering and Physical Sciences Research Council


Thomson, H., Yang, S., & Cochran, S. (2022). Machine Learning Enabled Quantitative Ultrasound Techniques for Tissue Differentiation. Journal of Medical Ultrasonics, 49, 517-528.



quantitative ultrasound, ultrasound phantoms, tissue characterization, parametric imaging, binary classifier, machine learning

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