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Visual servoing for a US‐guided therapeutic HIFU system by coagulated lesion tracking: a phantom study
Author(s) -
Seo Joonho,
Koizumi Norihiro,
Funamoto Takakazu,
Sugita Naohiko,
Yoshinaka Kiyoshi,
Nomiya Akira,
Homma Yukio,
Matsumoto Yoichiro,
Mitsuishi Mamoru
Publication year - 2011
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.394
Subject(s) - imaging phantom , computer science , computer vision , artificial intelligence , high intensity focused ultrasound , tracking (education) , match moving , speckle pattern , motion compensation , biomedical engineering , ultrasound , motion (physics) , nuclear medicine , medicine , radiology , psychology , pedagogy
Background Applying ultrasound (US)‐guided high‐intensity focused ultrasound (HIFU) therapy for kidney tumours is currently very difficult, due to the unclearly observed tumour area and renal motion induced by human respiration. In this research, we propose new methods by which to track the indistinct tumour area and to compensate the respiratory tumour motion for US‐guided HIFU treatment. Methods For tracking indistinct tumour areas, we detect the US speckle change created by HIFU irradiation. In other words, HIFU thermal ablation can coagulate tissue in the tumour area and an intraoperatively created coagulated lesion (CL) is used as a spatial landmark for US visual tracking. Specifically, the condensation algorithm was applied to robust and real‐time CL speckle pattern tracking in the sequence of US images. Moreover, biplanar US imaging was used to locate the three‐dimensional position of the CL, and a three‐actuator system drives the end‐effector to compensate for the motion. Finally, we tested the proposed method by using a newly devised phantom model that enables both visual tracking and a thermal response by HIFU irradiation. Results In the experiment, after generation of the CL in the phantom kidney, the end‐effector successfully synchronized with the phantom motion, which was modelled by the captured motion data for the human kidney. The accuracy of the motion compensation was evaluated by the error between the end‐effector and the respiratory motion, the RMS error of which was approximately 2 mm. Conclusion This research shows that a HIFU‐induced CL provides a very good landmark for target motion tracking. By using the CL tracking method, target motion compensation can be realized in the US‐guided robotic HIFU system. Copyright © 2011 John Wiley & Sons, Ltd.