  Ultrasound reflection tomography is widely used to image large complex
specimens that are only accessible from a single side, such as well systems and
nuclear power plant containment walls. Typical methods for inverting the
measurement rely on delay-and-sum algorithms that rapidly produce
reconstructions but with significant artifacts. Recently, model-based
reconstruction approaches using a linear forward model have been shown to
significantly improve image quality compared to the conventional approach.
However, even these techniques result in artifacts for complex objects because
of the inherent non-linearity of the ultrasound forward model.
  In this paper, we propose a non-iterative model-based reconstruction method
for inverting measurements that are based on non-linear forward models for
ultrasound imaging. Our approach involves obtaining an approximate estimate of
the reconstruction using a simple linear back-projection and training a deep
neural network to refine this to the actual reconstruction. We apply our method
to simulated ultrasound data and demonstrate dramatic improvements in image
quality compared to the delay-and-sum approach and the linear model-based
reconstruction approach.
