Deep learning for fast denoising filtering in ultrasound localization microscopy

Abstract

Abstract Objective. Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning. Approach. In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in both in vitro flow phantom experiment and in vivo experiment of New Zealand rabbit tumor. Main results. For in vitro flow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. For in vivo animal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24 μm and two microvessels separated by 46 μm could be clearly displayed. Most importantly,, the CS-Net denoising speeds for in vitro and in vivo experiments were 0.041 s frame−1 and 0.062 s frame−1, respectively. Significance. DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM.