TY - GEN
T1 - Deep Learning-based 3D Beamforming on a 2D Row Column Addressing (RCA) Array for 3D Super-resolution Ultrasound Localization Microscopy
AU - Kim, Jihun
AU - Dong, Zhijie
AU - Lowerison, Matthew R.
AU - Sekaran, Nathiya V.Chandra
AU - You, Qi
AU - Llano, Daniel A.
AU - Song, Pengfei
N1 - Funding Information:
ACKNOWLEDGMENT This study was partially supported by the National Institute of Biomedical Imaging and Bioengineering, the National Institute on Aging of the National Institutes of Health, and the Department of Defense (DoD) through the Breast Cancer Research Program (BCRP) under grant numbers R21EB030072, R21AG077173, and E01 W81XWH2110062. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Department of Defense.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We report 3D ULM imaging by using a 2D row-column addressing (RCA) array which achieves fast imaging volume rate. Furthermore, we propose a deep-learning (DL)-based adaptive beamforming method to improve the spatial resolution and contrast of the microbubble (MB) signal imaged by the RCA array. We evaluated the proposed technique on a wire phantom and MBs suspended in water. Moreover, we carried out an in vivo study on a mouse brain and demonstrated improved 3D ULM imaging based on the DL-beamformer. These results demonstrate that DL-based beamforming provides a viable solution for enhancing the RCA imaging quality for robust ULM.
AB - We report 3D ULM imaging by using a 2D row-column addressing (RCA) array which achieves fast imaging volume rate. Furthermore, we propose a deep-learning (DL)-based adaptive beamforming method to improve the spatial resolution and contrast of the microbubble (MB) signal imaged by the RCA array. We evaluated the proposed technique on a wire phantom and MBs suspended in water. Moreover, we carried out an in vivo study on a mouse brain and demonstrated improved 3D ULM imaging based on the DL-beamformer. These results demonstrate that DL-based beamforming provides a viable solution for enhancing the RCA imaging quality for robust ULM.
KW - 3D ULM
KW - RCA array
KW - deep learning-based beamforming
KW - super-resolution ultrasound imaging
UR - http://www.scopus.com/inward/record.url?scp=85143816546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143816546&partnerID=8YFLogxK
U2 - 10.1109/IUS54386.2022.9958375
DO - 10.1109/IUS54386.2022.9958375
M3 - Conference contribution
AN - SCOPUS:85143816546
T3 - IEEE International Ultrasonics Symposium, IUS
BT - IUS 2022 - IEEE International Ultrasonics Symposium
PB - IEEE Computer Society
T2 - 2022 IEEE International Ultrasonics Symposium, IUS 2022
Y2 - 10 October 2022 through 13 October 2022
ER -