Dual Generative Adversarial Network For Ultrasound Localization Microscopy

Abstract

Ultrasound localization microscopy (ULM) is a new imaging technique that uses microbubbles (MBs) to improve the spatial resolution of ultrasound (US) imaging. For ULM, it is critical to accurately localize MB position. Recently, deep learning-based methods are adopted to acquire MB localization, which shows promising performance and efficient computation. However, detection of high-concentration MBs is still a challenging task. To further improve the localization accuracy, a dual generative adversarial network (DualGAN)-based ULM imaging method (DualGAN-ULM) is proposed in this paper to overcome the problems of long data processing time and low parameter robustness in current ULM imaging methods. This method is trained using simulated data generated by point spread function (PSF) convolution and uses dual generation adversarial strategy to enable the generator to perform accurate localization under high-concentration MB conditions. Meanwhile, the localization and reconstruction capabilities of five ULM methods, namely Centroid, CS-ULM, mUNET-ULM, mSPCN-ULM and DualGAN-ULM, are evaluated in this paper. The experimental results reveal that DL-based ULM methods (DualGAN-ULM, mSPCN-ULM, and mUNET-ULM) outperform compressed sensing-based localization methods (CS-ULM) and Centroid in terms of localization accuracy and localization dependability. DualGAN-ULM performs better than mSPCN-ULM and mUNET-ULM, making it a more realistic ULM method.