Multi-Focus Ultrasound Imaging Using Generative Adversarial Networks

Ultrasound (US) beam can be focused at multiple locations to increase the lateral resolution of the resulting images. However, this improvement in resolution comes at the expense of a loss in frame rate, which is essential in many applications such as imaging moving anatomy. Herein, we propose a novel method based on Generative Adversarial Network (GAN) for achieving multi-focus line-per-line US image without a reduction in the frame rate. Results on simulated phantoms as well as real phantom experiments show that the proposed deep learning framework is able to substantially improve the resolution without sacrificing the frame rate.