Publications
Optical Flow Assisted Super-Resolution Ultrasound Localization Microscopy using Deep Learning
- AUTHORS
- Hyeonjik Lee1, Seok-Hwan Oh1, Myeong-Gee Kim1, Young-Min Kim1, Guil Jung1, Hyeon-Min Bae1
- PUBLISHED
- IEEE, International Symposium on Biomedical Imaging (ISBI)
- 1. Department of Electrical Engineering, KAIST, Daejeon, South Korea
Abstract:
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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