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Servoing Performance Enhancement via a Respiratory Organ Motion Prediction Model for a Non-Invasive Ultrasound Theragnostic System
Author(s) -
Tatsuya Fujii,
Norihiro Koizumi,
Atsushi Kayasuga,
Dongjun Lee,
Hiroyuki Tsukihara,
Hiroyuki Fukuda,
Kiyoshi Yoshinaka,
Takashi Azuma,
Hideyo Miyazaki,
Naohiko Sugita,
Kazushi Numata,
Yukio Homma,
Yoichiro Matsumoto,
Mamoru Mitsuishi
Publication year - 2017
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2017.p0434
Subject(s) - visual servoing , ultrasound , focus (optics) , respiratory system , computer vision , respiratory monitoring , motion (physics) , computer science , radiology , artificial intelligence , medicine , anatomy , physics , optics , image (mathematics)
[abstFig src='/00290002/15.jpg' width='300' text='Proposed method for tracking and following respiratory organ motion' ] High intensity focused ultrasound (HIFU) is potentially useful for treating stones and/or tumors. With respect to HIFU therapy, it is difficult to focus HIFU on the focal lesion due to respiratory organ motion, and this increases the risk of damaging the surrounding healthy tissues around the target focal lesion. Thus, this study proposes a method to cope with the fore-mentioned problem involving tracking and following the respiratory organ motion via a visual feedback and a prediction model for respiratory organ motion to realize highly accurate servoing performance for focal lesions. The prediction model is continuously updated based on the latest organ motion data. The results indicate that respiratory kidney motion of two healthy subjects is successfully tracked and followed with an accuracy of 0.88 mm by the proposed method and the constructed system.

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