Real-time and High Quality Ultrasound Elastography Using Convolutional Neural Network by Incorporating Analytic Signal

Convolutional Neural Networks (CNN) have been extensively used for many computer vision applications including optical flow estimation. Although CNNs have been very successful in optical flow problem, they have been rarely used for displacement estimation in Ultrasound Elastography (USE) due to vast differences between ultrasound data and computer vision images. In USE, a main goal is to obtain the strain image which is the derivative of the axial displacement in axial direction; therefore, a very accurate displacement estimation is required. Radio Frequency (RF) data is needed to obtain accurate displacement estimation. RF data contains high frequency contents which cannot be downsampled without significant loss of information, in contrast to computer vision images. We propose a novel technique to utilize LiteFlowNet for USE. For the first time, we incorporate analytic signal to improve the quality of the displacement estimation. We show that this network with the designed inputs is more suitable for USE compared to more complex networks such as FlowNet2. The network is adopted to our application and it is compared with FlowNet2 and a state-of-the-art elastography method (GLUE). The results show that this network performs well and comparable to GLUE. Furthermore, not only this network is faster and has lower memory footprint compared to FlowNet2, but also it obtains higher quality strain images which makes it suitable for portable and real-time elastography devices.