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Article: Learning-Based Efficient Phase- Amplitude Modulation and Hybrid Control for MRI-Guided Focused Ultrasound Treatment

TitleLearning-Based Efficient Phase- Amplitude Modulation and Hybrid Control for MRI-Guided Focused Ultrasound Treatment
Authors
KeywordsBeam motion control
hybrid control
learning-based modulation
robot-assisted MRg-FUS
Issue Date4-Dec-2023
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Robotics and Automation Letters, 2023, v. 9, n. 2, p. 995-1002 How to Cite?
AbstractMagnetic 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 H∞ 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.
Persistent Identifierhttp://hdl.handle.net/10722/346173

 

DC FieldValueLanguage
dc.contributor.authorDai, Jing-
dc.contributor.authorZhu, Bohao-
dc.contributor.authorWang, Xiaomei-
dc.contributor.authorJiang, Zhiyi-
dc.contributor.authorWu, Mengjie-
dc.contributor.authorLiang, Liyuan-
dc.contributor.authorXie, Xiaochen-
dc.contributor.authorLam, James-
dc.contributor.authorChang, Hing Chiu-
dc.contributor.authorKwok, Ka Wai-
dc.date.accessioned2024-09-12T00:30:39Z-
dc.date.available2024-09-12T00:30:39Z-
dc.date.issued2023-12-04-
dc.identifier.citationIEEE Robotics and Automation Letters, 2023, v. 9, n. 2, p. 995-1002-
dc.identifier.urihttp://hdl.handle.net/10722/346173-
dc.description.abstractMagnetic 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 H∞ 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.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Robotics and Automation Letters-
dc.subjectBeam motion control-
dc.subjecthybrid control-
dc.subjectlearning-based modulation-
dc.subjectrobot-assisted MRg-FUS-
dc.titleLearning-Based Efficient Phase- Amplitude Modulation and Hybrid Control for MRI-Guided Focused Ultrasound Treatment-
dc.typeArticle-
dc.identifier.doi10.1109/LRA.2023.3339090-
dc.identifier.scopuseid_2-s2.0-85179791325-
dc.identifier.volume9-
dc.identifier.issue2-
dc.identifier.spage995-
dc.identifier.epage1002-
dc.identifier.eissn2377-3766-
dc.identifier.issnl2377-3766-

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