IEEE Trans Med Imaging. 2023 May;42(5):1462-1471. doi: 10.1109/TMI.2022.3230635. Epub 2023 May 2.
Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. However, the lateral strain, which is essential in several downstream tasks such as the inverse problem of elasticity imaging, remains a challenge. The lateral strain estimation is complicated since the motion and the sampling frequency in this direction are substantially lower than the axial one, and a lack of carrier signal in this direction. In computer vision applications, the axial and the lateral motions are independent. In contrast, the tissue motion pattern in USE is governed by laws of physics which link the axial and lateral displacements. In this paper, inspired by Hooke's law, we, first propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose a constraint on the Effective Poisson's ratio (EPR) to improve the lateral strain estimation. In the next step, we propose self-supervised PICTURE (sPICTURE) to further enhance the strain image estimation. Extensive experiments on simulation, experimental phantom and in vivo data demonstrate that the proposed methods estimate accurate axial and lateral strain maps.
卷积神经网络 (CNN) 在超声弹性成像 (USE) 的位移估计中显示出了很有前景的结果。已经提出了许多修改方法来改进用于轴向 USE 的 CNN 的位移估计。然而，在弹性成像反问题等几个下游任务中至关重要的横向应变仍然是一个挑战。由于该方向的运动和采样频率远低于轴向，并且该方向缺乏载波信号，因此横向应变估计变得复杂。在计算机视觉应用中，轴向和横向运动是独立的。相比之下，USE 中的组织运动模式受物理定律支配，这些定律将轴向和横向位移联系起来。在本文中，受胡克定律的启发，我们首先提出了基于物理约束的无监督正则化弹性成像（PICTURE），其中我们对有效泊松比 (EPR) 施加约束以提高横向应变估计。在下一步中，我们提出了自监督 PICTURE（sPICTURE）来进一步增强应变图像估计。在模拟、实验体模和体内数据上的广泛实验表明，所提出的方法可以准确估计轴向和横向应变图。
