Abstract
Magnetic resonance imaging-guided acoustic trapping is expected to manipulate drug carriers (e.g., microbubbles) within the body, potentially improving carrier concentration at tumor sites and thereby enhancing targeted therapy outcomes. However, accurate trap generation remains challenging due to complex wave propagation through multiple tissue materials. Moreover, respiration-induced tissue motion imposes stringent requirements on computational efficiency for rapid phase updates. Here we propose a machine learning-based model and a closed-loop control scheme to modulate phase patterns rapidly. The model delivers precise time-of-flight prediction (mean err. ≤ 0.24 μs) within 26 ms for 196 transducer elements. In proof-of-concept experiments, computer vision feedback permits fast (about 15 frames per second) position adjustment of a trapped polystyrene ball (Ø2.7 mm). This control scheme helps lessen the ball’s spatial drift induced by time-varying multi-medium environments. These experiments on robotic manipulation support our model’s potential for future magnetic resonance imaging-guided targeted therapy.
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Data availability
The detailed data sets are available at a public GitHub repository (https://github.com/mengjwu/acoustictrap3D).
Code availability
The source code is available at a public GitHub repository (https://github.com/mengjwu/acoustictrap3D).
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Acknowledgements
This work is supported in part by National Natural Science Foundation of China (Grant No. 12504533), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515110927) and Guangdong University Featured Innovation Project (Grant No. 2025KTSCX129); in part by Shaanxi Provincial Department of Education (Grant No. 24JS024). We sincerely thank Prof. Huang Lixi for his valuable suggestions on the experiments. We are also grateful to Prof. Kwok Ka-Wai and Dr. Dai Jing for their early insights that contributed to the development of the idea.
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Mengjie Wu is with Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China. M. Wu conceptualized the study, designed the learning model and data analysis, developed the stereo vision algorithm, designed the chamber and printed circuit board, conducted the COMSOL study and manipulation experiments, and drafted and edited the manuscript. Xiaohan Li is with School of Information and Control Engineering, Xi’An University of Architecture and Technology, Xi’An, China. X. Li designed the stereo vision algorithm, recommended camera modules, and revised the manuscript. Tianquan Tang is with Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China, and also with School of Mechatronic Engineering and Automation, Foshan University, Foshan, China. T. Tang is the corresponding author (tianquan@connect.hku.hk). T. Tang conceptualized the study, designed the chamber, advised on experiments, and edited the manuscript.
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Communications Engineering thanks Gordon Dobie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: [Liangfei Tian] and [Philip Coatsworth]. A peer review file is available.
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Wu, M., Li, X. & Tang, T. Machine learning-facilitated real-time acoustic trapping in time-varying multi-medium environments toward magnetic resonance imaging-guided microbubble manipulation. Commun Eng (2026). https://doi.org/10.1038/s44172-026-00600-z
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DOI: https://doi.org/10.1038/s44172-026-00600-z
