Optical Flow Assisted Super-Resolution Ultrasound Localization Microscopy using Deep Learning
2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)(2022)
摘要
Ultrasound localization microscopy provides resolution enhanced ultrasound images and demonstrates clinical potential in myocardial infarction and diabetes. The conventional model-driven methods localize the microbubble by tracing the peak of the point spread function. Such numerical schemes demonstrate weakness in identifying superimposed microbubbles, indicating the limitations for super-resolution (SR) images. Recently, learning-based approaches have been studied for precise localization of densely distributed microbubbles. However, prior arts reconstruct the SR images from static B-mode images, which results in inconsistent localization of microbubbles across sequential frames. In this paper, we propose a temporal relational ultrasound microscopy network (TRUM-Net). The TRUM-Net adopts optical flow estimation of consecutive frames and a feedback loop for detailed super-resolution imaging. The proposed scheme enhances the accuracy of microbubble localization by 25.8% and the structural similarity up to 54.9%.
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关键词
Ultrasound localization microscopy,super-resolution,optical flow,feedback loop
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