Delaunay, R;
Hu, Y;
Vercauteren, T;
(2022)
An unsupervised learning-based shear wave tracking method for ultrasound elastography.
In:
Proceedings of SPIE Medical Imaging: Medical Imaging 2022: Ultrasonic Imaging and Tomography.
Society of Photo-Optical Instrumentation Engineers (SPIE)
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | An unsupervised learning-based shear wave tracking method for ultrasound elastography |
| Event: | Medical Imaging 2022: Ultrasonic Imaging and Tomography |
| Location: | San Diego, CA, USA |
| Dates: | 20th February - 28th March 2022 |
| ISBN-13: | 9781510649514 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2612200 |
| Publisher version: | https://doi.org/10.1117/12.2612200 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Shear wave elastography (SWE), Acoustic radiation force, deep learning, recurrent neural network, Long Short Term Memory |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151211 |
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