Zayed Abdelrahman, Cloutier Guy, Rivaz Hassan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2027-2030. doi: 10.1109/EMBC44109.2020.9176625.
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.
超声弹性成像通过监测组织对内部或外部力的响应来估计其力学特性。根据不同组织类型的力学特性，会产生不同程度的变形，其中较硬的组织变形较小。给定在某种变形前后采集的两个射频（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。平面内轴向运动会产生高质量的应变图像，而平面外运动会产生低质量的应变图像。在本文中，我们介绍了一种使用卷积神经网络（CNN）的新方法，该方法仅需5.4毫秒就能确定一对RF帧是否适合用于弹性成像。我们的方法还可用于自动选择最佳的一对RF帧，从而生成高质量的应变图像。CNN在3818对RF帧上进行了训练，同时在986对新的未见数据对上进行了测试，准确率超过91%。RF帧是从体模和体内数据中采集的。
