论文标题
在超声弹性图中使用CNN的自动框架选择
Automatic Frame Selection using CNN in Ultrasound Elastography
论文作者
论文摘要
超声弹性图用于通过监测其对内力或外力的反应来估计组织的机械性能。不同水平的变形从不同的组织类型中获得，具体取决于其机械性能，在该特性中，组织变形更少。给定两个射频（RF）在某些变形之前和之后收集的射频（RF）帧，我们通过比较RF帧来估计位移和应变图像。应变图像的质量取决于变形过程中发生的运动类型。平面轴向运动会产生高质量的应变图像，而平面外运动则导致低质量应变图像。在本文中，我们使用卷积神经网络（CNN）介绍了一种新方法，以确定仅在5.4 ms中对弹性造影的一对RF帧的适用性。我们的方法还可以用于自动选择最佳的RF帧，从而产生高质量的应变图像。 CNN通过3,818对RF帧进行了培训，而测试是在986对新看见的两对中进行的，其准确性超过91％。从幻影和体内数据收集了RF帧。
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force. Different levels of deformation are obtained from different tissue types depending on their mechanical properties, where stiffer tissues deform less. Given two radio frequency (RF) frames collected before and after some deformation, we estimate displacement and strain images by comparing the RF frames. The quality of the strain image is dependent on the type of motion that occurs during deformation. In-plane axial motion results in high-quality strain images, whereas out-of-plane motion results in low-quality strain images. In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image. The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. The RF frames were collected from both phantom and in vivo data.
