  Objective: Ultrasound elastography is gaining traction as an accessible and
useful diagnostic tool for such things as cancer detection and differentiation
and thyroid disease diagnostics. Unfortunately, state of the art shear wave
imaging techniques, essential to promote this goal, are limited to high-end
ultrasound hardware due to high power requirements; are extremely sensitive to
patient and sonographer motion, and generally, suffer from low frame rates.
Motivated by research and theory showing that longitudinal wave sound speed
carries similar diagnostic abilities to shear wave imaging, we present an
alternative approach using single sided pressure-wave sound speed measurements
from channel data.
  Methods: In this paper, we present a single-sided sound speed inversion
solution using a fully convolutional deep neural network. We use simulations
for training, allowing the generation of limitless ground truth data.
  Results: We show that it is possible to invert for longitudinal sound speed
in soft tissue at high frame rates. We validate the method on simulated data.
We present highly encouraging results on limited real data.
  Conclusion: Sound speed inversion on channel data has significant potential,
made possible in real time with deep learning technologies.
  Significance: Specialized shear wave ultrasound systems remain inaccessible
in many locations. longitudinal sound speed and deep learning technologies
enable an alternative approach to diagnosis based on tissue elasticity. High
frame rates are possible.
