Augmented reality elastography ultrasound via generate adversarial network for breast cancer diagnosis

Abstract Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. We therefore present a cost-efficient solution by designing a deep neural network to synthesize augmented reality EUS (AR-EUS) from conventional B-mode images. By using 4580 cases from 15 medical centers, we evaluate the performance of AR-EUS on breast cancer diagnosis. The quantitative metric and blind evaluation results show no significant difference between AR-EUS and real EUS in image authenticity and in clinical diagnosis. The performance of pocket-sized ultrasound in breast tumor diagnosis is also significantly improved after AR-EUS is equipped. These results highlight the potential of AR-EUS in clinical application.