Ultrasound Super Resolution using Vision Transformer with Convolution Projection Operation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Ultrasound Localization Microscopy (ULM) refers to a technique using ultra-fast consecutive frames to create a super resolution image beyond the diffraction limit. As one of general steps for ULM, ultrasound microbubble (MB) localization directly affects the image performance. The traditional localization methods suffer from the lake of robustness and computational inefficiency. To solve these problems, we propose a transformer based convolutional neural network to make an end-to-end mapping to localize the microbubbles. The performance of the proposed method is validated on data from Ultrasound Localisation and TRacking Algorithms for Super Resolution (ULTRA-SR). The results showed that our proposed network achieved high precision and Jaccard index. These benefits can be used to further improve the image visualization and processing efficiency.

Original languageEnglish (US)
Title of host publicationIUS 2022 - IEEE International Ultrasonics Symposium
PublisherIEEE Computer Society
ISBN (Electronic)9781665466578
StatePublished - 2022
Event2022 IEEE International Ultrasonics Symposium, IUS 2022 - Venice, Italy
Duration: Oct 10 2022Oct 13 2022

Publication series

NameIEEE International Ultrasonics Symposium, IUS
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727


Conference2022 IEEE International Ultrasonics Symposium, IUS 2022

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics


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