Effect of Freezing and Fixation on Quantitative Ultrasound Parameters in Phantoms of Brain and Brain Tumour

Thomson, Hannah; Yang, Shufan; Cochran, Sandy; Stritch, Thomas; Baldwin, Mitch

doi:10.1109/ius46767.2020.9251780

Cited by 1 publication

(2 citation statements)

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“…There is a need for a real-time imaging tool to aid neurosurgeons in complete removal of brain tumours. Many brain tumours, particularly gliomas, are extremely difficult to resect fully because of their infiltrative nature and similarity to healthy brain tissue [35]. This study determined the acoustic properties of chicken liver and gizzard muscle and, through comparison with data from the literature, showed that these tissues were suitable phantoms for brain and brain tumour, respectively, in terms of density, speed of sound, and attenuation.…”

Section: Discussionmentioning

confidence: 91%

“…With regard to future work, the present study made the assumption that a good macroscopic ultrasound phantom corresponds to a good QUS phantom. A further study provided evidence of using those parameters for characterising brain and brain tumour tissue [35]. However, due to the difficulty of obtaining human fresh brain and brain tumour samples, the effect of brain and brain tumour tissue's inhomogeneity in QUS parameters is still unknown.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-enabled quantitative ultrasound techniques for tissue differentiation

2022

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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.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%