  Computational simulation of ultrasound (US) echography is essential for
training sonographers. Realistic simulation of US interaction with microscopic
tissue structures is often modeled by a tissue representation in the form of
point scatterers, convolved with a spatially varying point spread function.
This yields a realistic US B-mode speckle texture, given that a scatterer
representation for a particular tissue type is readily available. This is often
not the case and scatterers are nontrivial to determine. In this work we
propose to estimate scatterer maps from sample US B-mode images of a tissue, by
formulating this inverse mapping problem as image translation, where we learn
the mapping with Generative Adversarial Networks, using a US simulation
software for training. We demonstrate robust reconstruction results, invariant
to US viewing and imaging settings such as imaging direction and center
frequency. Our method is shown to generalize beyond the trained imaging
settings, demonstrated on in-vivo US data. Our inference runs orders of
magnitude faster than optimization-based techniques, enabling future extensions
for reconstructing 3D B-mode volumes with only linear computational complexity.
