Ultrasound Localization Microscopy Using Deep Neural Network

Abstract

Noninvasive imaging of microvascular structures in deep tissues provides morphological and functional information for clinical diagnosis and monitoring. Ultrasound Localization Microscopy (ULM) is an emerging imaging technique that can generate microvascular structures with subwavelength diffraction resolution. However, the clinical utility of ULM is hindered by technical limitations, such as long data acquisition time, high microbubble concentration, and inaccurate localization. In this paper, we propose a Swin transformer-based neural network to perform end-to-end mapping to implement microbubble localization. The performance of the proposed method was validated using synthetic and in vivo data using different quantitative metrics. The results indicate that our proposed network can achieve higher precision and better imaging capability than previously used methods. Furthermore, the computational cost of processing per frame is three to four times faster than traditional methods, which makes the real-time application of this technique feasible in the future.