论文标题
端到端应变正则化的超声弹性图的无监督方法
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation
论文作者
论文摘要
准静态超声弹力图（使用）是一种成像方式，它包括确定响应于应用机械力的软组织变形（即晶格）的度量。通常是通过估计在应用手动压缩之前和之后获得的连续超声框架之间的位移来确定的。位移预测的计算效率和准确性，也称为时间估计，是实时使用应用程序的关键挑战。在本文中，我们提出了一种新颖的深度学习方法，用于超声射频（RF）数据之间有效的时间估计。所提出的方法由卷积神经网络（CNN）组成，该网络（CNN）预测一对后压缩前和后压缩超声RF框架之间的位移场。通过优化参考图像和压缩图像之间的相似性度量，以无监督的方式训练网络。我们还引入了一个新的正则化项，该项通过直接优化应变平滑度来保留位移连续性。我们通过使用超声模拟和健康志愿者的体内数据来验证方法的性能。我们还将方法的性能与称为Overwind [17]的最先进方法进行了比较。在30个模拟中，我们方法的平均对比度比（CNR）和信噪比（SNR）和3个体内图像对分别为7.70和6.95、7和0.31。我们的结果表明，我们的方法可以有效地预测准确的应变图像。我们方法的无监督方面代表了使用深度学习应用来分析临床超声数据的巨大潜力。
Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e.strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric be-tween the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data.
