论文标题
用于超声图像模拟的散射器分布估算的深网络
Deep Network for Scatterer Distribution Estimation for Ultrasound Image Simulation
论文作者
论文摘要
基于仿真的超声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超声图像外观可以建模为具有代表组织微结构的点散射器的点扩散函数的卷积。但是，这种散射器分布通常不知道，其对给定组织类型的估计在根本上是一个不良的反问题。在本文中，我们展示了一种卷积神经网络方法，用于从观察到的超声数据中进行概率散射器估计。我们在这里建议通过训练合成图像上的卷积神经网络来了解超声图像和分布参数图之间的映射，并在散点子上施加已知的统计分布。与几种现有方法相比，我们在数值模拟和体内图像中证明了与我们的方法估计的散点子表示的合成图像，与我们的方法估计的观测值与不同的采集参数（例如成像域的压缩和旋转）非常匹配。
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.
