Fast Multi-Focus Ultrasound Image Recovery Using Generative Adversarial Networks

Abstract

In conventional ultrasound (US) imaging, it is common to transmit several focused beams at multiple locations to generate a multi-focus image with constant lateral resolution throughout the image. However, this method comes at the expense of a loss in temporal resolution, which is important in applications requiring both high-frame rate and constant lateral resolution. Moreover, relative motions of the target with respect to the probe often exist due to hand tremors or biological motions, causing blurring artifacts in the multi-focus image. This article introduces a novel approach for multi-focus US image recovery based on Generative Adversarial Network (GAN) without a reduction in the frame-rate. Herein, a mapping function between the single-focus US image and multi-focus version for having a constant lateral resolution everywhere is estimated through different GANs. We use adversarial loss functions in addition to Mean Square Error (MSE) to generate more realistic ultrasound images. Moreover, we use the boundary seeking method for improving the stability of training, which is currently the main challenge in using GANs. Experiments on simulated and real phantoms as well as on ex vivo data are performed. Results confirm that having both adversarial loss function and boundary seeking training provides better results in terms of the mean opinion score test. Furthermore, the proposed method enhances the resolution and contrast indexes without sacrificing the frame-rate. As for the comparison with other approaches which are not based on NNs, the proposed approach gives similar results while requiring neither channel data nor computationally expensive algorithms.