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Sarah Grube

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Proceedings Article | 3 April 2023 Poster + Paper

Ultrasound shear wave velocity estimation in a small field of view via spatio-temporal deep learning

Sarah Grube, Marcel Bengs, Maximilian Neidhardt, Sarah Latus, Alexander Schlaefer

Proceedings Volume 12464, 1246425 (2023) https://doi.org/10.1117/12.2653833

KEYWORDS: Acoustic waves, Tissues, Deep learning, Ultrasonography, Error analysis, Education and training, Elasticity, Laparoscopy, Gelatin, Elastography

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A change in tissue stiffness can indicate pathological diseases and therefore supports physicians in diagnosis and treatment. Ultrasound shear wave elastography (US-SWEI) can be used to quantify tissue stiffness by estimating the velocity of propagating shear waves. While a linear US probe with a lateral imaging width of approximately 40 mm is commonly used and US-SWEI has been successfully demonstrated, some clinical applications, such as laparoscopic or endoscopic interventions, require small probes. This limits the lateral image width to the millimeter range and reduces the available information in the US images substantially. In this work, we systematically analyze the effect of a reduced lateral imaging width for shear wave velocity estimation using the conventional time-of-flight (ToF) method and spatio-temporal convolutional neural networks (ST-CNNs). For our study, we use tissue mimicking gelatin phantoms with varying stiffness and resulting shear wave velocities in the range from 3.63 m/s to 7.09 m/s. We find that lateral imaging width has a substantial impact on the performance of ToF, while shear wave velocity estimation with ST-CNNs remains robust. Our results show that shear wave velocity estimation with ST-CNN can even be performed for a lateral imaging width of 2.1 mm resulting in a mean absolute error of 0.81 ± 0.61 m/s.

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