An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation

TL;DR

This paper introduces an unsupervised deep learning method for ultrasound elastography that efficiently estimates tissue displacement and strain, improving accuracy and real-time applicability in clinical settings.

Contribution

It presents a novel CNN-based approach with strain regularization for unsupervised displacement estimation in ultrasound elastography, enhancing accuracy and computational efficiency.

Findings

Achieved high contrast-to-noise and signal-to-noise ratios in simulations and in vivo data.

Outperformed the state-of-the-art OVERWIND method in key metrics.

Demonstrated potential for clinical ultrasound data analysis.

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 be-tween 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.