Delaunay, R;
Hu, Y;
Vercauteren, T;
(2020)
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation.
In: Martel, A.L. et al., (ed.)
Proceedings of the 23rd International Conference on Medical Image Computing & Computer Assisted Intervention.
Springer: Cham.
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Abstract
Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e. strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric between the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data.
| Type: | Proceedings paper |
|---|---|
| Title: | An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation |
| Event: | 23rd International Conference on Medical Image Computing & Computer Assisted Intervention: MICCAI 2020 |
| Dates: | 04 October 2020 - 08 October 2020 |
| ISBN: | 978-3-030-59715-3 |
| ISBN-13: | 978-3-030-59716-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-59716-0_55 |
| Publisher version: | https://doi.org/10.1007/978-3-030-59716-0_55 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Ultrasound elastography, Time delay estimation, Convolutional neural network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10108460 |
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