Deep reconstruction of high-quality ultrasound images from raw plane-wave data: A simulation and in vivo study

This paper presents a novel beamforming approach based on deep learning to get closer to the ideal Point Spread Function (PSF) in Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct a high-quality version of Tissue Reflectivity Function (TRF) from echo traces acquired by transducer elements using only a single plane-wave transmission. In this approach, first, a model for the TRF is introduced by setting the imaging PSF as an isotropic (i.e., circularly symmetric) 2D Gaussian kernel convolved with a cosine function. Then, a mapping function between the pre-beamformed Radio-Frequency (RF) channel data and the proposed output is constructed using deep learning. Network architecture contains multi-resolution decomposition and reconstruction using wavelet transform for effective recovery of high-frequency content of the desired output. We exploit step by step training from coarse (mean square error) to fine (ℓ_0.2) loss functions. The proposed method is trained on 1174 simulation ultrasound data with the ground-truth echogenicity map extracted from real photographic images. The performance of the trained network is evaluated on the publicly available simulation and in vivo test data without any further fine-tuning. Simulation test results show an improvement of 37.5% and 65.8% in terms of axial and lateral resolution as compared to Delay-And-Sum (DAS) results, respectively. The contrast is also improved by 33.7% in comparison to DAS. Furthermore, the reconstructed in vivo images confirm that the trained mapping function does not need any fine-tuning in the new domain. Therefore, the proposed approach maintains high resolution, contrast, and framerate simultaneously.