Deep Learning-Based Microbubble Localization for Ultrasound Localization Microscopy

Xi Chen, Matthew R. Lowerison, Zhijie Dong, Aiguo Han, Pengfei Song

Research output: Contribution to journalArticlepeer-review


Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)-based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radio frequency (RF) and in-phase and quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% compared to conventional localization. In addition, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of this article support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.

Original languageEnglish (US)
Pages (from-to)1312-1325
Number of pages14
JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Issue number4
StatePublished - Apr 1 2022


  • Deep learning (DL)
  • microbubble (MB)
  • super-resolution
  • ultrasound imaging
  • ultrasound localization microscopy (ULM)

ASJC Scopus subject areas

  • Instrumentation
  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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