Beamforming with deep learning from single plane wave RF data

Deep learning approaches for improving ultrasound (US) image reconstruction have proven successful in both experimental and clinical settings. In this paper, we present an autoencoder-based deep learning framework for ultrasound beamforming from the radio-frequency (RF) data of a single plane wave. Motivated by U-Net, the network consists of an encoder and a decoder. The network is trained and evaluated with simulated and \textit{in vivo} datasets. When tested on simulated data, the beamformed images from the proposed network achieved an SNR of 25.02, contrast of -40.61dB, and gCNR of 1.0, a mean PSNR of 18.64dB compared to ground truth images and mean axial and lateral FWHM of 0.42 and 0.61 respectively. Each of these metrics outperformed the standard delay and sum (DAS) beamforming algorithm. In addition, the network was evaluated on an \textit{in vivo} breast mass, achieving improved SNR of 3.81. These results demonstrate that the proposed network is capable of generating high quality US images from only one plane wave, which could be applied to multiple ultrasound-based clinical tasks.