System Identification Method for Non-Invasive Ultrasound Theragnostic System Incorporating Mechanical Oscillation Part
Norihiro Koizumi*, Kouhei Oota*, Dongjun Lee*,
Hiroyuki Tsukihara**, Akira Nomiya**, Kiyoshi Yoshinaka***,
Takashi Azuma*, Naohiko Sugita*, Yukio Homma**,
Yoichiro Matsumoto*, and Mamoru Mitsuishi*
*School of Engineering, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
**School of Medicine, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
***National Institute of Advanced Industrial Science and Technology (AIST), 1-2-1 Namiki, Tsukuba, Ibaraki 305-8564, Japan
In this paper, we propose a method for identifying systems incorporating a mechanical oscillation part for a non-invasive ultrasound theragnostic system(NIUTS). The NIUTS tracks and follows movement in an area requiring treatment (renal stones, in this study) by irradiating the area with high intensity focused ultrasound (HIFU). Blur noise caused by oscillation of the mechanical system adversely affects the servo performance. To solve this problem and enhance the servo performance, it is first necessary to identify those parts of the NIUTS system that incorporate a mechanical oscillation part. Secondly, we implemented a mechanical oscillation suppression filter based on the abovementioned method for identifying the mechanical oscillation part.
-  J. E. Kennedy, et al., “High-intensity focused ultrasound: surgery of the future?,” in British Journal of Radiology, Vol.76, pp. 590-599, 2003.
-  J. G. Lynn, R. L. Zwemer, A. J. Chick, and A. E. Miller, “A new method for the generation and use of focused ultrasound in experimental biology,” in J. Gen. Physiol., Vol.26, pp. 179-193, 1942.
-  T. Ikeda, S. Yoshizawa, M. Tosaki, J. S. Allen, S. Takagi, N. Ohta, T. Kitamura, and Y. Matsumoto, “Cloud Cavitation Control for Lithotripsy Using High Intensity Focused Ultrasound,” in Ultrasound Med Biol, Vol.32, No.9, pp. 1383-1397, 2006.
-  F. Wu, Z. L. Wang, Z. Zhang, et al., “Acute biological effects of high-intensity focused ultrasound on H22 liver tumours in vivo,” in Chin. Ultrasound Med., Vol.13, No.3, 1997.
-  G. Tu, T. Y. Qiao, and S. He, “An experimental study on highintensity focused ultrasound in the treatment of VX-2 rabbit kidney tumours,” in Chin. J. Urol., Vol.20, No.8, 1999.
-  F. Wu, W. Z. Chen, J. Bai, J. Z. Zou, Z. L. Wang, H. Zhu, and Z. B. Wang, “Pathological changes in malignant carcinoma treated with high-intensity focused ultrasound,” in Ultrasound Med. Biol. Vol.27, No.8, pp. 1099-1106, 2001.
-  J. E. Kennedy, et al., “High-intensity focused ultrasound for the treatment of liver tumours,” in Ultrasound Med. Biol. Vol.42, pp. 931-935, 2004.
-  N. Koizumi, J. Seo, T. Funamoto, A. Nomiya, A. Ishikawa, K. Yoshinaka, N. Sugita, Y. Homma, Y. Matsumoto, and M. Mitsuishi, “Technologizing and Digitalizing Medical Professional Skills for a Non-Invasive Ultrasound Theragnostic System,” in Journal of Robotics and Mechatronics, Vol.24, No.2, pp. 379-388, 2012.
-  F. Pene, E. Courtine, A. Cariou, and, J.P. Mira, “Toward theranostics,” in Crit Care Med, Vol.37, pp. S50-S58, 2009.
-  N. Koizumi, H. Tsukihara, S. Takamoto, H. Hashizume, and M. Mitsuishi, “Robot vision technology for technologizing and digitalization of medical diagnostic and therapeutic skills,” in International Journal of Automation Technology, Vol.3, No.5, pp. 541-550, 2009.
-  K. Yano, E. Ohara, S. Horihata, T. Aoki, and Y.Nishimoto, “Development of tremor suppression control system using adaptive filter and its application to meal-assist robot,” SICE Journal of Control, Measurement, and System Integration, Vol.45, No.12, pp. 638-645, 2009.
-  C. N. Riviere, Wei-Tech. Ang, and P. K. Khosla, “Filtering involuntary motion of people with tremor disability using optimal equalization,” in IEEE Trans. on Robotics and Automation, Vol.15, No.5, pp. 793-800, 2003.
-  S. Pledgie, K. E. Barner, S. K. Agrawal, and T. Rahman, “Tremor suppression through impedance control,” in IEEE Trans on Rehabilitation and Engineering, Vol.8, No.1, pp. 53-59, 2000.
-  J. G. Gonzalez, E. A. Heredia, T. Rahman, K. E. Barner, and G. R. Arce, “Filtering involuntary motion of people with tremor disability using optimal equalization,” in Proc. IEEE Int. Conf. On Systems, Man and Cybernetics, pp. 2402-2407, 1995.
-  N. Koizumi, K. Oota, A. Nomiya, H. Tsukihara, K. Yoshinaka, T. Azuma, N. Sugita, Y. Homma, Y. Matsumoto, and M. Mitsuishi, “Mechanical System Identification Method for Non-Invasive Ultrasound Theragnostic System,” in Procedia CIRP – First CIRP Conference on BioManufacturing (BioM2013) –, Mamoru Mitsuishi and Paolo Bartolo (Eds), Vol.5, pp. 315-320, 2013.
-  M. Pernot et al., “3-D Real-Time Motion Correction in High-Intensity Focused Ultrasound Therapy,” Ultrasound in Med. Biol., Vol.30, No.9, pp. 1239-1249, 2004.
-  P. Abolmaesumi, S. E. Salcudean, W. H. Zhu, M. Sirouspour, and S. DiMaio, “Image-guided control of a robot for medical ultrasound,” in IEEE Trans. Robotics and Automation, Vol.18, pp. 11-23, 2002.
-  A. Krupa, G. Fichtinger, and G. Hager, “Real-time motion stabilization with B-mode ultrasound image speckle information and visual servoing,” in International Journal of Robotics Research, Vol.28, pp. 1334-1354, 2009.
-  D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, M. A. Quigley, and M. E. Meyerand, “Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data,” in Am. J. Neuroradiol., Vol.22, No.7, pp. 1326-1333, 2001.
-  J. A. McAteer, J. C. Williams, A. P. Evan, R. O. Cleveland, J. V. Cauwelaert, M. R. Bailey, and D. A. Lifshitz, “Ultracal-30 gypsum artificial stones for research on the mechanisms of stone breakage in shock wave lithotripsy,” in Urol Res, Vol.33, pp. 429-434, 2005.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.