TY - GEN
T1 - Deep learning based microbubble localization for fast and robust ultrasound localization microscopy
AU - Chen, Xi
AU - Lowerison, Matthew R.
AU - Dong, Zhijie
AU - Sekaran, Nathiya Vaithiyalingam Chandra
AU - Zhang, Wei
AU - Llano, Daniel A.
AU - Song, Pengfei
N1 - Funding Information:
ACKNOWLEDGMENT This study was supported by the National Cancer Institute of the National Institutes of Health under Award Number
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/7
Y1 - 2020/9/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85097905023&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097905023&partnerID=8YFLogxK
U2 - 10.1109/IUS46767.2020.9251542
DO - 10.1109/IUS46767.2020.9251542
M3 - Conference contribution
AN - SCOPUS:85097905023
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2020 - International Ultrasonics Symposium, Proceedings
PB - IEEE Computer Society
T2 - 2020 IEEE International Ultrasonics Symposium, IUS 2020
Y2 - 7 September 2020 through 11 September 2020
ER -