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

TY - CHAP

T1 - Super-Resolved Micro-Vascular Imaging Using Microbubbles and Machine Learning

AU - Christensen-Jeffries, Kirsten

AU - Schirmer, Markus

AU - Browning, Richard

AU - Tang, Meng-Xing

AU - Dunsby, Christopher

AU - Aljabar, Paul

AU - Eckersley, Robert

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Microbubbles

KW - SUPER-RESOLUTION

KW - ULTRASOUND

KW - Machine learning

M3 - Conference paper

SP - 108

BT - IEEE EMBS 2014 Micro and Nanotechnology in Medicine Conference

ER -