DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hyeonjik | ko |
dc.contributor.author | Oh, Seok-Hwan | ko |
dc.contributor.author | Kim, Myeong-Gee | ko |
dc.contributor.author | Kim, Young-Min | ko |
dc.contributor.author | Jung, Guil | ko |
dc.contributor.author | Bae, Hyeon-Min | ko |
dc.date.accessioned | 2022-12-22T03:02:39Z | - |
dc.date.available | 2022-12-22T03:02:39Z | - |
dc.date.created | 2022-12-21 | - |
dc.date.created | 2022-12-21 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | 2022 IEEE International Ultrasonics Symposium, IUS 2022 | - |
dc.identifier.issn | 1948-5719 | - |
dc.identifier.uri | http://hdl.handle.net/10203/303494 | - |
dc.description.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%. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Optical Flow Assisted Super-Resolution Ultrasound Localization Microscopy using Deep Learning | - |
dc.type | Conference | - |
dc.identifier.wosid | 000896080400206 | - |
dc.identifier.scopusid | 2-s2.0-85143783981 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 2022 IEEE International Ultrasonics Symposium, IUS 2022 | - |
dc.identifier.conferencecountry | IT | - |
dc.identifier.conferencelocation | Venice Convention Center | - |
dc.identifier.doi | 10.1109/IUS54386.2022.9957762 | - |
dc.contributor.localauthor | Bae, Hyeon-Min | - |
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