Direct Reconstruction of Ultrasound Elastography Using an End-to-End Deep Neural Network

In this work, we developed an end-to-end convolutional neural network (CNN) to reconstruct the ultrasound elastography directly from radio frequency (RF) data. The novelty of this network is able to infer the distribution of elastography from real RF data by only using computational simulation as the training data. Moreover, this framework can generate displacement and strain field respectively both from ultrasound RF data directly. We evaluated the performance of this network on 50 simulated RF datasets, 42 phantom datasets, and 4 human datasets. The best results of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in simulated data, phantom data, and human data are 39.5 dB and 69.64 dB, 32.64 dB and 48.76 dB, 23.24 dB and 46.22 dB, respectively. Furthermore, we also compare the performance of our method to the state-of-art ultrasound elastography using normalized cross-correlation (NCC) technique. From this comparison, it shows that that our method can effectively compute the strain field robustly and accurately in the this paper. These results might imply great potential of this deep learning method in ultrasound elastography application.