IEEE Trans Ultrason Ferroelectr Freq Control. 2022 Jul;69(7):2236-2247. doi: 10.1109/TUFFC.2022.3171676. Epub 2022 Jun 30.
High-quality motion estimation is essential for ultrasound elastography (USE). Traditional motion estimation algorithms based on speckle tracking such as normalized cross correlation (NCC) or regularization such as global ultrasound elastography (GLUE) are time-consuming. In order to reduce the computational cost and ensure the accuracy of motion estimation, many convolutional neural networks have been introduced into USE. Most of these networks such as radio-frequency modified pyramid, warping and cost volume network (RFMPWC-Net) are supervised and need many ground truths as labels in network training. However, the ground truths are laborious to collect for USE. In this study, we proposed a MaskFlownet-based unsupervised convolutional neural network (MF-UCNN) for fast and high-quality motion estimation in USE. The inputs to MF-UCNN are the concatenation of RF, envelope, and B-mode images before and after deformation, while the outputs are the axial and lateral displacement fields. The similarity between the predeformed image and the warped image (i.e., the postdeformed image compensated by the estimated displacement fields) and the smoothness of the estimated displacement fields were incorporated in the loss function. The network was compared with modified pyramid, warping and cost volume network (MPWC-Net)++, RFMPWC-Net, GLUE, and NCC. Results of simulations, breast phantom, and in vivo experiments show that MF-UCNN obtains higher signal-to-noise ratio (SNR) and higher contrast-to-noise ratio (CNR). MF-UCNN achieves high-quality motion estimation with significantly reduced computation time. It is unsupervised and does not need any ground truths as labels in the training, and, thus, has great potential for motion estimation in USE.
高质量的运动估计对于超声弹性成像（USE）至关重要。基于散斑跟踪的传统运动估计算法，如归一化互相关（NCC）或正则化，如全局超声弹性成像（GLUE），都很耗时。为了降低计算成本并确保运动估计的准确性，许多卷积神经网络已被引入到 USE 中。这些网络中的大多数，如射频修正金字塔、变形和代价体网络（RFMPWC-Net），都是基于监督学习的，需要许多真实数据作为网络训练的标签。然而，对于 USE 来说，收集真实数据是很费力的。在这项研究中，我们提出了一种基于掩膜飞行网络（MaskFlownet）的无监督卷积神经网络（MF-UCNN），用于快速、高质量的 USE 运动估计。MF-UCNN 的输入是变形前后的射频、包络和 B 模式图像的串联，而输出是轴向和横向位移场。在损失函数中，考虑了预变形图像和变形图像（即，通过估计的位移场补偿的后变形图像）之间的相似性以及估计的位移场的平滑度。该网络与修正金字塔、变形和代价体网络（MPWC-Net）++、RFMPWC-Net、GLUE 和 NCC 进行了比较。模拟、乳腺仿体和体内实验的结果表明，MF-UCNN 获得了更高的信噪比（SNR）和更高的对比噪声比（CNR）。MF-UCNN 实现了高质量的运动估计，同时大大减少了计算时间。它是无监督的，在训练中不需要任何真实数据作为标签，因此在 USE 中的运动估计具有很大的潜力。
