Conference paper
Ultrasound Multiple Point Target Detection and Localization using Deep Learning
Department of Health Technology, Technical University of Denmark1
Biomedical Engineering, Department of Health Technology, Technical University of Denmark2
Center for Fast Ultrasound Imaging, Biomedical Engineering, Department of Health Technology, Technical University of Denmark3
Mems Applied Sensors Group, Biomedical Engineering, Department of Health Technology, Technical University of Denmark4
Department of Electrical Engineering, Technical University of Denmark5
Polymer Cell, Immunobiology and Biomimetics, Department of Health Technology, Technical University of Denmark6
Immunobiology and Biomimetics, Department of Health Technology, Technical University of Denmark7
Super-resolution imaging (SRI) can achieve subwavelength resolution by detecting and tracking intravenously injected microbubbles (MBs) over time. However, current SRI is limited by long data acquisition times since the MB detection still relies on diffraction-limited conventional ultrasound images.
This limits the number of detectable MBs in a fixed time duration. In this work, we propose a deep learning-based method for detecting and localizing high-density multiple point targets from radio frequency (RF) channel data. A Convolutional Neural Network (CNN) was trained to return confidence maps given RF channel data, and the positions of point targets were estimated from the confidence maps.
RF channel data for training and evaluation were simulated in Field II by placing point targets randomly in the region of interest and transmitting three steered plane waves. The trained CNN achieved a precision and recall of 0.999 and 0.960 on a simulated test dataset. The localization errors after excluding outliers were within ± 46 µm and ± 27 µm in the lateral and axial directions.
A scatterer phantom was 3-D printed and imaged by the Synthetic Aperture Real-time Ultrasound System (SARUS). On measured data, a precision and recall of 0.976 and 0.998 were achieved, and the localization errors after excluding outliers were within ± 101 µm and ± 75 µm in the lateral and axial directions.
We expect that this method can be extended to highly concentrated microbubble (MB) detection in order to accelerate SRI.
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 1937-1940 |
Proceedings: | 2019 IEEE International Ultrasonics Symposium |
ISBN: | 1728145961 , 172814597X , 172814597x , 9781728145969 , 9781728145976 , 1728145953 and 9781728145952 |
ISSN: | 19485727 |
Types: | Conference paper |
DOI: | 10.1109/ultsym.2019.8925914 |
ORCIDs: | Youn, Jihwan , Ommen, Martin Lind , Stuart, Matthias Bo , Thomsen, Erik Vilain , Larsen, Niels Bent and Jensen, Jørgen Arendt |