论文标题
在无监督的正规化弹力中，使用自我监督和身体灵感的约束进行横向应变成像
Lateral Strain Imaging using Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography
论文作者
论文摘要
卷积神经网络（CNN）在超声弹力图中的位移估计（使用）表现出了令人鼓舞的结果。已经提出了许多修改以改善CNN的位移估计，以供轴向方向使用。但是，在几个下游任务（例如弹性成像的反问题）中至关重要的侧向应变仍然是一个挑战。横向应变估计是复杂的，因为该方向的运动和采样频率大大低于轴向，并且在该方向上缺乏载体信号。在计算机视觉应用中，轴向和横向运动是独立的。相比之下，使用的组织运动模式受物理定律的控制，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，我们首先提出了对无监督的正则弹性图（图片）的身体启发的限制，在该约束中，我们对有效的泊松比（EPR）施加了限制，以改善侧向应变估计。在下一步中，我们提出了自我监督的图片（Spicture），以进一步增强应变图像估计。关于仿真，实验幻影和体内数据的广泛实验表明，所提出的方法估计了准确的轴向和横向应变图。
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.
