An unsupervised learning-based shear wave tracking method for ultrasound elastography

Rémi Delaunay; Yipeng Hu; Tom Vercauteren

doi:10.1117/12.2612200

4 April 2022 An unsupervised learning-based shear wave tracking method for ultrasound elastography

Rémi Delaunay, Yipeng Hu, Tom Vercauteren

Proceedings Volume 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography; 120380N (2022) https://doi.org/10.1117/12.2612200
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work investigates the use of convolutional neural networks to improve displacement estimation accuracy in shear wave imaging. Our training approach is completely unsupervised, which allows to learn the estimation of the induced micro-scale deformations without ground truth labels. We also present an ultrasound simulation dataset where the shear wave propagation has been simulated via finite element method. Our dataset is made publicly available along with this paper, and consists in 150 shear wave propagation simulations in both homogenous and hetegeneous media, which represents a total of 20,000 ultrasound images. We assessed the ability of our learning-based approach to characterise tissue elastic properties (i.e., Young's modulus) on our dataset and compared our results with a classical normalised cross-correlation approach.

Conference Presentation

Citation Download Citation

Rémi Delaunay, Yipeng Hu, and Tom Vercauteren "An unsupervised learning-based shear wave tracking method for ultrasound elastography", Proc. SPIE 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography, 120380N (4 April 2022); https://doi.org/10.1117/12.2612200

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