  The performance of ultrasound elastography (USE) heavily depends on the
accuracy of displacement estimation. Recently, Convolutional Neural Networks
(CNN) have shown promising performance in optical flow estimation and have been
adopted for USE displacement estimation. Networks trained on computer vision
images are not optimized for USE displacement estimation since there is a large
gap between the computer vision images and the high-frequency Radio Frequency
(RF) ultrasound data. Many researchers tried to adopt the optical flow CNNs to
USE by applying transfer learning to improve the performance of CNNs for USE.
However, the ground truth displacement in real ultrasound data is unknown, and
simulated data exhibits a domain shift compared to the real data and is also
computationally expensive to generate. To resolve this issue, semi-supervised
methods have been proposed wherein the networks pre-trained on computer vision
images are fine-tuned using real ultrasound data. In this paper, we employ a
semi-supervised method by exploiting the first and second-order derivatives of
the displacement field for the regularization. We also modify the network
structure to estimate both forward and backward displacements, and propose to
use consistency between the forward and backward strains as an additional
regularizer to further enhance the performance. We validate our method using
several experimental phantom and in vivo data. We also show that the network
fine-tuned by our proposed method using experimental phantom data performs well
on in vivo data similar to the network fine-tuned on in vivo data. Our results
also show that the proposed method outperforms current deep learning methods
and is comparable to computationally expensive optimization-based algorithms.
