Biplane US-Guided Real-Time Volumetric Target Pose Estimation Method for Theragnostic HIFU System

Joonho Seo; Norihiro Koizumi; Takakazu Funamoto; Naohiko Sugita; Kiyoshi Yoshinaka; Akira Nomiya; Yukio Homma; Yoichiro Matsumoto; Mamoru Mitsuishi

doi:10.20965/jrm.2011.p0400

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JRM Vol.23 No.3 pp. 400-407

doi: 10.20965/jrm.2011.p0400

(2011)

Paper:

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Biplane US-Guided Real-Time Volumetric Target Pose Estimation Method for Theragnostic HIFU System

Joonho Seo^, Norihiro Koizumi^, Takakazu Funamoto^,
Naohiko Sugita^, Kiyoshi Yoshinaka^, Akira Nomiya^,
Yukio Homma^, Yoichiro Matsumoto^,
and Mamoru Mitsuishi^*

^*Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

^**Graduate School of Medicine, The University of Tokyo, Hongo, Tokyo, Japan

Received:

September 30, 2010

Accepted:

April 13, 2011

Published:

June 20, 2011

Keywords:

biplane US, volume target pose estimation, US guided HIFU system

Abstract

This paper presents a real-time pose estimation method as a part of robotic HIFU treatment system for moving volumetric targets. For the acquired biplane US images, current pose of the preoperative model is calculated by iterative segmentation and registration. Seed contours for the segmentation in each iteration is provided by previously registered preoperative 3-D model. The segmented boundary points then update the pose of 3-D model. The boundary outlier-removal makes the algorithm robust against partially noisy boundaries as well as the spatial boundary points accelerates the algorithm to be calculated in real-time. By the phantom experiments, registration accuracy for a biplane US image data was evaluated, and the processing time was also investigated.

Cite this article as:

J. Seo, N. Koizumi, T. Funamoto, N. Sugita, K. Yoshinaka, A. Nomiya, Y. Homma, Y. Matsumoto, and M. Mitsuishi, “Biplane US-Guided Real-Time Volumetric Target Pose Estimation Method for Theragnostic HIFU System,” J. Robot. Mechatron., Vol.23 No.3, pp. 400-407, 2011.

Data files:

References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] S. C. Davies, A. L. Hill, R. B. Holmes,M. Halliwell, and P. C. Jackson, “Ultrasound Quantitation of Respiratory Organ Motion in the Upper Abdomen,” British J. of Radiology Vol.67, pp. 1096-1102, 1994.

[2] [2] N. Pagoulatos, W. S. Edwards, D. R. Haynor, and Y. Kim, “Interactive 3-D Registration of Ultrasound and Magnetic Resonance Images Based on a Magnetic Position Sensor,” IEEE Trans. Inform. Technol. Biomed., Vol.3, No.4, pp. 278-288, Dec. 1999.

[3] [3] A. Roche, X. Pennec, G. Malandain, and N. Ayache, “Rigid Registration of 3D Ultrasound with MR Images: a New Approach Combining Intensity and Gradient Information,” IEEE Trans. on Medical Imaging, Vol.20, pp. 1038-1049, 2001.

[4] [4] A. Leroy, P.Mozer, Y. Payan, and J. Troccaz, “Intensity-Based Registration of Freehand 3D Ultrasound and CT-Scan Images of the Kidney,” Int. J. of Computer Assisted Radiology and Surgery, Vol.2 No.1, pp. 31-41, 2007.

[5] [5] W. Weina, S. Brunkeb, A. Khamenea, M. R. Callstromc, and N. Navabd, “Automatic CT-Ultrasound Registration for Diagnostic Imaging and Image-Guided Intervention,” Medical Image Analysis, Vol.12, No.5, pp. 577-585, 2008.

[6] [6] B. Zeng, R. Ishikawa, T. Oishi, J. Takamatsu, and K. Ikeuchi, “6-DOF Pose Estimation from Single Ultrasound Image Using 3D IP Models,” Proc. of the SPIE International Symposium on Medical Imaging 2006, pp. 227-237, 2008.

[7] [7] D. P. Luebke, “A Developer’s Survey of Polygonal Simplification Algorithms,” IEEE Computer Graphics and Applications’01, pp. 24-35, 2001.

[8] [8] A. Nuchter, K. Lingemann, and J. Hertzberg, “Cached KD-Tree Search for ICP Algorithms,” Proc. of the Sixth Int. Conf. on 3-D Digital Imaging and Modeling, pp. 419-426, August 21-23, 2007.

[9] [9] A. Ahmad, D. W. Cool, B. H. Chew, S. E. Pautler, and T. M. Peters, “3D Segmentation of Kidney Tumors from Freehand 2D Ultrasound,” Proc. of the SPIE Int. Symposium on Medical Imaging 2006, pp. 227-237, 2006.

[10] [10] B. L. Obadia and A. Gee, “Adaptive Segmentation of Ultrasound Images,” Image and vision Computing J., Vol.17, No.8, pp. 583-588, 1999.

[11] [11] J. A. Noble, “Ultrasound Image Segmentation: A Survey,” IEEE Trans. on Medical Imaging, Vol.25, No.8, pp. 987-1010, 2006.

[12] [12] M. M. Frenandez and C. A. Lopez, “An Approach for Contour Detection of Human Kidneys from Ultrasound Images Using Markov Random Fields and Active Contours,” Medical Image Analysis, Vol.9, pp. 1-23, 2005.

[13] [13] R. Larsen, M. Nielsen, and J. Sporring, “Prostate Segmentation in 2D Ultrasound Images Using Image Warping and Ellipse Fitting,” LNCS 4191, pp. 17-24, 2006.

[14] [14] L. A. Piegl and W. Tiller, “Least-Squares B-Spline Curve Approximation with Arbitrary End Derivatives,” Engineering with Computer, Vol.16, No.2, pp. 109-116, 2000.

[15] [15] K. Low, “Linear Least-Square Optimization for Point-to-Plane ICP Surface Registration,” Technical report TR04-004, UNC, 2004.

Biplane US-Guided Real-Time Volumetric Target Pose Estimation Method for Theragnostic HIFU System

Joonho Seo*, Norihiro Koizumi*, Takakazu Funamoto*, Naohiko Sugita*, Kiyoshi Yoshinaka*, Akira Nomiya**, Yukio Homma**, Yoichiro Matsumoto*, and Mamoru Mitsuishi*

Joonho Seo^, Norihiro Koizumi^, Takakazu Funamoto^,
Naohiko Sugita^, Kiyoshi Yoshinaka^, Akira Nomiya^,
Yukio Homma^, Yoichiro Matsumoto^,
and Mamoru Mitsuishi^*