  Shear-wave elastography (SWE) permits local estimation of tissue elasticity,
an important imaging marker in biomedicine. This recently-developed, advanced
technique assesses the speed of a laterally-travelling shear wave after an
acoustic radiation force "push" to estimate local Young's moduli in an
operator-independent fashion. In this work, we show how synthetic SWE (sSWE)
images can be generated based on conventional B-mode imaging through deep
learning. Using side-by-side-view B-mode/SWE images collected in 50 patients
with prostate cancer, we show that sSWE images with a pixel-wise mean absolute
error of 4.5+/-0.96 kPa with regard to the original SWE can be generated.
Visualization of high-level feature levels through t-Distributed Stochastic
Neighbor Embedding reveals substantial overlap between data from two different
scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode
images obtained with a scanner without SWE functionality. We also examined the
use of this type of network in elasticity imaging in the thyroid. Limitations
of the technique reside in the fact that networks have to be retrained for
different organs, and that the method requires standardization of the imaging
settings and procedure. Future research will be aimed at development of sSWE as
an elasticity-related tissue typing strategy that is solely based on B-mode
ultrasound acquisition, and the examination of its clinical utility.
