A generative adversarial network for ultrasound signal enhancement by transforming low-voltage beamformed radio frequency data to high-voltage data

Abstract

Ultrasound image quality is strongly dependent on penetration depth and attenuation. The transmit voltage in ultrasonic systems can be increased to increase output power and improve the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in deeper regions. However, the utility of using high transmit voltages and thus high output power is limited due to associated thermal and mechanical bioeffects. Additionally, the ability to increase the output power is limited in portable and low-cost ultrasound devices which have lower power. We propose a software-based approach, using a conditional generative adversarial network (cGAN) to amplify signals in deeper regions and enhance image quality without increasing the transmit voltage. The cGAN was customized and trained with beamformed radio frequency phantom data pairs (n=288) acquired with a Verasonics Vantage System; with input data taken at a low output voltage (20V) and corresponding output data taken at a high output voltage (70V). We trained and tested the performance of different loss functions and cGAN architectures. Our proposed model, tested on a hold-out phantom data set (n=73) was able to improve the average penetration depth by roughly 16% (1 cm gain in penetration depth) when compared to the low-voltage images. We found an average increase in CNR of 160.45%±117.64, increase in Peak SNR of 5675%±1.89dB, and increase in SNR of 32.68%±4.84 dB, relative to the low voltage images, for selected hyper and hypoechoic regions of interest. This work has potential applications in fetal imaging, where safety guidelines limit transmit voltage significantly, and portable devices.