Image Quality Enhancement Using an Improved Deep Neural Network for Single Plane Wave Beamforming

Abstract

In recent years, plane wave (PW) ultrasound (US) imaging has emerged as a promising method for ultrafast US imaging due to its high temporal resolution. PW transmissions have a lower image quality when the number of PW is limited. The conventional approach to reconstruction entails a coherently summing sequence of US signals at the cost of frame rate. In this paper, an improved deep neural network approach is applied for reconstructing a high-quality PW image from a single PW radio frequency (RF) signal. Specifically, the deformable convolution is utilized as a feature extractor added on the U-net network, and a discriminator is utilized to improve the network fitting effect. It aims to learn a mapping between a single PW and compounding 75 PWs by training the network with in vitro and in vivo samples. The performance of the proposed approach is evaluated in terms of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). Extensive quantitative and visual evaluations reveal that the proposed model improves PSNR by 32.97 percent and 21.24 percent in cross-section and longitudinal sections of the carotid artery respectively, compared to the U-net. The results suggest the potential of reconstructing high-quality images from a single PW via deep learning.