Wen Shuojie, Peng Bo, Wei Xingyue, Luo Jianwen, Jiang Jingfeng
IEEE Trans Ultrason Ferroelectr Freq Control. 2023 May;70(5):354-367. doi: 10.1109/TUFFC.2023.3243539. Epub 2023 Apr 26.
Accurate and computationally efficient motion estimation is a critical component of real-time ultrasound strain elastography (USE). With the advent of deep-learning neural network models, a growing body of work has explored supervised convolutional neural network (CNN)-based optical flow in the framework of USE. However, the above-said supervised learning was often done using simulated ultrasound data. The research community has questioned whether simulated ultrasound data containing simple motion can train deep-learning CNN models that can reliably track complex in vivo speckle motion. In parallel with other research groups' efforts, this study developed an unsupervised motion estimation neural network (UMEN-Net) for USE by adapting a well-established CNN model named PWC-Net. Our network's input is a pair of predeformation and postdeformation radio frequency (RF) echo signals. The proposed network outputs both axial and lateral displacement fields. The loss function consists of a correlation between the predeformation signal and the motion-compensated postcompression signal, smoothness of the displacement fields, and tissue incompressibility. Notably, an innovative correlation method known as the globally optimized correspondence (GOCor) volumes module developed by Truong et al. was used to replace the original Corr module to enhance our evaluation of signal correlation. The proposed CNN model was tested using simulated, phantom, and in vivo ultrasound data containing biologically confirmed breast lesions. Its performance was compared against other state-of-the-art methods, including two deep-learning-based tracking methods (MPWC-Net++ and ReUSENet) and two conventional tracking methods (GLUE and BRGMT-LPF). In summary, compared with the four known methods mentioned above, our unsupervised CNN model not only obtained higher signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) for axial strain estimates but also improved the quality of the lateral strain estimates.
准确且计算高效的运动估计是实时超声应变弹性成像（USE）的关键组成部分。随着深度学习神经网络模型的出现，越来越多的工作在USE框架下探索基于监督卷积神经网络（CNN）的光流。然而，上述监督学习通常使用模拟超声数据进行。研究界质疑包含简单运动的模拟超声数据能否训练出能够可靠跟踪体内复杂散斑运动的深度学习CNN模型。与其他研究团队的努力并行，本研究通过改编一个名为PWC-Net的成熟CNN模型，开发了一种用于USE的无监督运动估计神经网络（UMEN-Net）。我们网络的输入是一对变形前和变形后的射频（RF）回波信号。所提出的网络输出轴向和横向位移场。损失函数由变形前信号与运动补偿后压缩信号之间的相关性、位移场的平滑度以及组织不可压缩性组成。值得注意的是，Truong等人开发的一种名为全局优化对应（GOCor）体积模块的创新相关方法被用于替代原始的Corr模块，以增强我们对信号相关性的评估。所提出的CNN模型使用包含经生物学证实的乳腺病变的模拟、体模和体内超声数据进行测试。其性能与其他先进方法进行了比较，包括两种基于深度学习的跟踪方法（MPWC-Net++和ReUSENet）以及两种传统跟踪方法（GLUE和BRGMT-LPF）。总之，与上述四种已知方法相比，我们的无监督CNN模型不仅在轴向应变估计中获得了更高的信噪比（SNR）和对比噪声比（CNR），而且还提高了横向应变估计的质量。
