Deep learning based microbubble localization for fast and robust ultrasound localization microscopy

Xi Chen, Matthew R. Lowerison, Zhijie Dong, Nathiya Vaithiyalingam Chandra Sekaran, Wei Zhang, Daniel A. Llano, Pengfei Song

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

Abstract

Ultrasound localization microscopy (ULM) is a recently developed technique that addresses the resolution-penetration trade-off of ultrasound. However, its clinical application was limited by localization performance. In this study, we propose to improve the localization performance of ULM with a deep learning based localization technique that uses Field-II simulation and RF data.

Original languageEnglish (US)
Title of host publicationIUS 2020 - International Ultrasonics Symposium, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781728154480
DOIs
StatePublished - Sep 7 2020
Event2020 IEEE International Ultrasonics Symposium, IUS 2020 - Las Vegas, United States
Duration: Sep 7 2020Sep 11 2020

Publication series

NameIEEE International Ultrasonics Symposium, IUS
Volume2020-September
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2020 IEEE International Ultrasonics Symposium, IUS 2020
CountryUnited States
CityLas Vegas
Period9/7/209/11/20

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

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