Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network

Wenting Gu; Boyi Li; Jianwen LuoZhuangzhi YanDean TaXin Liu

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Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network

机译：Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network

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Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL)-based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data ( ${L}$ frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging ${l}$ -frame raw US images with ${l}$ being significantly smaller than ${L}$ . To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows $sim 40$ -fold reduction in data acquisition time and $sim 61$ -fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time ( $sim 0.33$ s) and ultrafast data processing speed ( $sim 0.60$ s) that makes it promising to observe rapid biological activities in vivo.

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《IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control》 |2023年第1期|25-40|共16页
作者
Wenting Gu; Boyi Li; Jianwen LuoZhuangzhi YanDean TaXin Liu;
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作者单位

School of Communication and Information Engineering, Shanghai University, Shanghai, China;

Academy for Engineering and Technology, Fudan University, Shanghai, China;

Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaCenter for Biomedical Engineering and the Academy for Engineering and Technology, Fudan University, Shanghai, ChinaAcademy for Engineering and Technology and the State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China;

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正文语种英语
中图分类无线电设备、电信设备;
关键词
Generators; Imaging; Spatial resolution; Image reconstruction; Location awareness; Superresolution; Data acquisition;

Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network

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