  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.
