BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.16.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240802T110000
DTEND;TZID=Asia/Hong_Kong:20240802T120000
DTSTAMP:20260512T152757
CREATED:20240724T043745Z
LAST-MODIFIED:20250114T042912Z
UID:18932-1722596400-1722600000@ece.hku.hk
SUMMARY:High-Frame-Rate Ultrasound Imaging in the Deep Learning Era
DESCRIPTION:Please note the seminar venue is revised to Tam Wing Fan Innovation Wing Two\, The University of Hong Kong. \nAbstract\nUltrasound is undoubtedly a popular medical imaging modality and is becoming known for its high-frame-rate imaging capabilities. However\, high-frame-rate ultrasound has yet to flourish in point-of-care applications due to the lack of suitable portable hardware\, and its ability to offer time-resolved flow visualization is hampered by Doppler aliasing artifacts. Can we take advantage of deep learning to overcome bottlenecks in high-frame-rate system design? Can we design neural networks to resolve Doppler aliasing artifacts in real time? This seminar will introduce our laboratory’s quest to learn deep and learn smart about ultrasound imaging systems to make high-frame-rate ultrasound viable for portable use and flow estimation. We will demonstrate how deep learning solutions can be devised to resolve data transfer bottlenecks in ultrasound systems and\, in turn\, enable robust generation of high-frame-rate ultrasound images with data acquired from few array channels. We will also show how deep learning has enabled the design of advanced Doppler flow imaging platforms with lucid flow visualization performance. Related algorithms\, real-time engineering efforts\, and clinical applications will be presented throughout the presentation. \nSpeaker\nProf. Alfred C. H. YU\nAssistant Vice-President (Research and International)\,\nProfessor of Biomedical Engineering\,\nUniversity of Waterloo\, Canada \nBiography of the Speaker\nProf. Alfred C. H. YU is Assistant Vice-President (Research and International) and Professor of Biomedical Engineering at the University of Waterloo\, Canada. He leads the University of Waterloo’s research partnership portfolio and interdisciplinary research files\, and he is the Director of the NSERC Collaborative Research Program on “Next-Generation Innovations in Ultrasonics” in Canada. Alfred has a long-standing research interest in ultrasound imaging and therapeutics. He is a Fellow of IEEE\, American Institute of Ultrasound in Medicine\, Canadian Academy of Engineering\, and Engineering Institute of Canada. His research has been endorsed by many milestone prizes\, including the NSERC Steacie Memorial Fellowship\, the ISTU Frederic Lizzi Award\, the IEEE Ultrasonics Early Career Investigator Award\, the Ontario Early Researcher Award\, and various best paper awards. He is now the Editor-in-Chief of the IEEE Transactions on Ultrasonics\, Ferroelectrics\, and Frequency Control\, the Program Chair of 2023 and 2025 IEEE Ultrasonics Symposium\, and the Vice-Chair of the AIUM Basic Science and Instrumentation Group. \nOrganiser\nProf. W.-N. LEE \nCo-organisers\nIEEE EMB Hong Kong-Macau Joint Chapter\nTam Wing Fan Innovation Wing Two \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240802-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/07/1280-3.jpg
END:VEVENT
END:VCALENDAR