Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation

摘要

Simulation-based ultrasound (US) training can be an essential educational tool. Realistic US 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 article, we demonstrate a convolutional neural network approach for probabilistic scatterer estimation from observed US data. We herein propose to impose a known statistical distribution on scatterers and learn the mapping between US 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 amp;italicamp;in vivoamp;/italicamp; 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.