Ultrasound Super Resolution Using Deep Learning Based on Attention Mechanism

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

2 Scopus citations


Ultrasound Localization Microscopy (ULM) has gained a lot of interest as a new imaging technology capable of achieving subwave diffraction resolution. Currently, it is still challenging to achieve a high accuracy and robust localization in in-vivo dataset. Traditional single emitter localization methods, such as Gaussian fit, Radial Symmetry (RS) and average weight had problems with precision, robustness and computational efficiency. In this work, we propose an attention mechanism based neural network, namely ATT-net, to make an end-to-end mapping to localize the microbubbles and scale the input dimension. The performance of the proposed method is validated on in-silico and in-vivo data and compared with two other localization methods. The results showed that our proposed network achieved higher precision and Jaccard index. These benefits can be used to further improve the image visualization and processing efficiency.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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