论文标题
在卷积神经网络中注入已知的操作员，以在超声弹性学中进行横向应变成像
Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography
论文作者
论文摘要
卷积神经网络（CNN）已用于超声弹性图（使用）中的位移估计。高质量的轴向应变（轴向位移的衍生物在轴向方向上）可以通过建议的网络估算。与轴向应变相反，侧向应变（在泊松的比率成像和弹性重建中）的质量较差。主要原因包括较低的采样频率，有限的运动和横向方向缺乏相位信息。最近，已经提出了无监督的正则弹性图（图片）中受身体启发的约束。该方法考虑了由运动规则定义的可行侧向应变的范围，并采用正则化策略来改善侧向菌株。尽管有很大的改善，但正规化仅在培训期间应用。因此，它不能保证在测试期间横向应变在可行范围内。此外，仅采用可行范围，没有研究其他约束（例如不可压缩性）。在本文中，我们解决了这两个问题，并提出了Kpicture，其中将两种迭代算法以已知操作员的形式注入网络体系结构中，以确保侧向应变在可行的范围内，并在测试阶段施加不可压缩性。
Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In contrast to axial strain, lateral strain, which is highly required in Poisson's ratio imaging and elasticity reconstruction, has a poor quality. The main causes include low sampling frequency, limited motion, and lack of phase information in the lateral direction. Recently, physically inspired constraint in unsupervised regularized elastography (PICTURE) has been proposed. This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains. Despite the substantial improvement, the regularization was only applied during the training; hence it did not guarantee during the test that the lateral strain is within the feasible range. Furthermore, only the feasible range was employed, other constraints such as incompressibility were not investigated. In this paper, we address these two issues and propose kPICTURE in which two iterative algorithms were infused into the network architecture in the form of known operators to ensure the lateral strain is within the feasible range and impose incompressibility during the test phase.
