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 B-mode 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-and-sum(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.