Learning-Based Efficient Phase- Amplitude Modulation and Hybrid Control for MRI-Guided Focused Ultrasound Treatment
Jing Dai, Bohao Zhu, Xiaomei Wang, Zhiyi Jiang, Mengjie Wu, Liyuan Liang, Xiaochen Xie, James Lam
Abstract
Magnetic resonance-guided focused ultrasound (MRg-FUS) has become attractive, accredited to its non-invasive nature. However, ultrasound beams focusing and steering is still challenging owing to aberrations induced by soft tissue heterogeneity. In particular for beam motion control to ensure real-time and precise tracking in the deep-seated region over abdominal organs, while considering full-wave propagation. To this end, we proposed a closed-loop hybrid control scheme and a learning-based modulation model for robot-assisted MRg-FUS treatments. By introducing a rapid phase estimator to provide an efficient (<3 ms) solution, the robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller enables real-time and accurate tracking (0.30 mm) without prior knowledge of heterogeneous media, even under unknown disturbances. Our model enables rapid (2.65 ms) phase-amplitude modulation and precise targeting (mean 0.35 mm, max. 0.65 mm), meeting clinical standard. Focal obliquity is significantly “aligned” to only 2.7°. Results from sensitivity analysis and transducer design also support the model's clinical feasibility and potential in widespread MRg-FUS treatments.
BibTeX
@inproceedings{ral2024_learningbasedeff,
title = {Learning-Based Efficient Phase- Amplitude Modulation and Hybrid Control for MRI-Guided Focused Ultrasound Treatment},
author = {Jing Dai and Bohao Zhu and Xiaomei Wang and Zhiyi Jiang and Mengjie Wu and Liyuan Liang and Xiaochen Xie and James Lam and Hing-Chiu Chang and Ka-Wai Kwok},
booktitle = {RA-L 2024},
year = {2024}
}