High Frequency Ultrasound Image Recovery Using Tight Frame Generative Adversarial Networks

In ultrasound imaging, there is a trade-off between imaging depth and axial resolution because of physical limitations. Increasing the center frequency of the transmitted ultrasound wave improves the axial resolution of resulting image. However, High Frequency (HF) ultrasound has a shallower depth of penetration. Herein, we propose a novel method based on Generative Adversarial Network (GAN) for achieving a high axial resolution without a reduction in imaging depth. Results on simulated phantoms show that a mapping function between Low Frequency (LF) and HF ultrasound images can be constructed.