  Simulation-based ultrasound training can be an essential educational tool.
Realistic ultrasound image appearance with typical speckle texture can be
modeled as convolution of a point spread function with point scatterers
representing tissue microstructure. Such scatterer distribution, however, is in
general not known and its estimation for a given tissue type is fundamentally
an ill-posed inverse problem. In this paper, we demonstrate a convolutional
neural network approach for probabilistic scatterer estimation from observed
ultrasound data. We herein propose to impose a known statistical distribution
on scatterers and learn the mapping between ultrasound image and distribution
parameter map by training a convolutional neural network on synthetic images.
In comparison with several existing approaches, we demonstrate in numerical
simulations and with in-vivo images that the synthesized images from scatterer
representations estimated with our approach closely match the observations with
varying acquisition parameters such as compression and rotation of the imaged
domain.
