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 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.

中文翻译：

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用于超声图像仿真的散射体分布估计的深层网络

基于模拟的超声（US）培训可能是必不可少的教育工具。具有典型斑点纹理的逼真的美国图像外观可以建模为点扩散函数与代表组织微结构的点散射体的卷积。然而，这种散射体分布通常是未知的，并且对于给定组织类型的估计基本上是一个不适定的逆问题。在本文中，我们演示了一种卷积神经网络方法，用于根据观察到的美国数据进行概率散射体估计。我们在这里提出将已知的统计分布强加给散射体，并通过在合成图像上训练卷积神经网络来学习US图像和分布参数图之间的映射。与几种现有方法相比，我们在数值模拟和体内 图像，这些图像由我们的方法估计的散射体表示合成图像与观测值紧密匹配，并具有变化的采集参数，例如成像域的压缩和旋转。