A Conditional Adversarial Network for Single Plane Wave Beamforming

Compared to traditional focused transmissions, plane wave (PW) ultrasound imaging could enable higher frame rates by over a hundred fold, which is clinically relevant to real-time applications and ultrafast imaging. However, along with reducing acquisition time, PW imaging is confounded by image quality degradation, acoustic clutter, and speckle noise. To tackle this problem, we present a deep learning-based method to analyze raw radiofrequency (RF) channel data acquired by the ultrasound probe and convert this signal to the final Bmode image, bypassing the traditional beamforming procedure. The deep learning architecture relies on a conditional generative adversarial network (cGAN), in which the generative model and classifying model work simultaneously to produce an indistinguishable output from a ground truth. The cGAN was trained to predict B-mode images that look like beamformed PW results after multiple insonifications. This network was trained and tested utilizing a publicly accessible PICMUS database composed of in vivo and ex vivo ultrasound inclusions with randomly distributed scatterers in various combinations. The proposed method produces signal-to-noise ratio (SNR) enhancements from 1.112 to 1.540 when compared with conventional delay-andsum (DAS) beamforming of a single PW insonification. The cross correlation coefficient between a 75 plane wave image and cGAN-predicted data is 0.976, compared to 0.641 with DAS beamforming of a single PW insonification. These results demonstrate the feasibility of using this adversarial network to substitute traditional DAS beamforming in future applications.