  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.
