Super-Resolved Micro-Vascular Imaging Using Microbubbles and Machine Learning

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Recent developments in sub-diffraction ultrasound (US) imaging using clinical US systems has shown the potential to resolve structures on the micrometer scale using microbubbles (MBs). These rely on user-defined thresholds for MB identification making their clinical application challenging. Here, an automated post-processing algorithm based on k-means clustering has been developed to identify noise, individual and multiple MB in vivo without user interaction. This method has the potential to non-invasively image in real-time pathological or therapeutic changes in the micro-vasculature at centimeter depths in a clinical setting.
Original languageEnglish
Title of host publicationIEEE EMBS 2014 Micro and Nanotechnology in Medicine Conference
Number of pages1
Publication statusPublished - 2014


  • Microbubbles
  • Machine learning


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