A Laplacian Surface Deformation and Optimization Based 3D Registration Algorithm for Image Guided Prostate Radiotherapy

Abstract

Purpose: To develop a fast landmark-based deformable registration method to capture the soft tissue transformation between the planning 3D CT images and treatment 3D cone-beam CT (CBCT) images for the adaptive external beam radiotherapy (EBRT). Method and Materials: The developed method was based on a global-to-local landmark-based deformable registration algorithm. The landmarks were first acquired by applying a fast segmentation method using the active shape model. The global registration method was applied to establish a registration framework. The Laplacian surface deformation (LSD) and Laplacian surface optimization (LSO) method were then employed for local deformation and remeshing respectively to reach an optimal registration solution. In LSD, the deformed mesh is generated by minimizing the quadratic energy to keep the shape and to move control points to the target position. In LSO, a mesh is reconstructed by minimizing the quadratic energy to smooth the object by minimizing the difference while keeping the landmarks unchanged. The method was applied on 8 EBRT prostate datasets. The distance and volume based estimators were used to evaluate the results. The target volumes delineated by physicians were used as gold standards in the evaluation. Results: The entire segmentation and registration processing time was within 1 minute for all the datasets. The mean distance estimators ranged from 0.43 mm to 2.23 mm for the corresponding model points between the treatment CBCT images and the registered planning images. The mean overlap ratio ranged from 85.5% to 93.2% of the prostate volumes after registration. These results demonstrated reasonably good agreement between the developed method and the gold standards. Conclusion: A novel and fast landmark-based deformable registration method is developed to capture the soft tissue transformation between the planning and treatment images for prostate target volumes. The results show that with the method the image registration and transformation can be completed within one minute and has the potential to be applied to real-time adaptive radiotherapy.

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J. Zhou, S. Zhang, S. Kim, S. Jabbour, S. Goyal, B. Haffty, D. Metaxas and N. Yue, "A Laplacian Surface Deformation and Optimization Based 3D Registration Algorithm for Image Guided Prostate Radiotherapy," International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol. 1 No. 2, 2012, pp. 40-49. doi: 10.4236/ijmpcero.2012.12006.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, T. Murray, and M.J. Thun, “Cancer statistics”, CA: a cancer journal for clinicians, 58(2):71-96, (2008).
[2] L.J. Bos, J. van der Geer, M. van Herk, B.J. Mijnheer, J.V. Lebesque, and E.M. Damen, “The sensitivity of dose dis-tributions for organ motion and set-up uncertainties in prostate IMRT”, Radiother. Oncol., 76(1):18-26, (2005).
[3] T.S. Hong, W.A. Tome, R.J. Chappell, P. Chinnaiyan, M.P. Mehta and P.M. Harari, "The impact of daily setup variations on head-and-neck intensity-modulated radiation therapy", Int. J. Radiat. Oncol. Biol. Phys., 61(3):779-788, (2005).
[4] V. Rudat, P. Schraube, D. Oetzel, D. Zierhut, M. Flentje, and M. Wannenmacher, “Combined error of patient positioning variability and prostate motion uncertainty in 3D conformal radiotherapy of localized prostate cancer,” Int. J. Radiat. Oncol. Biol. Phys., 35(5):1027-1034 (1996).
[5] M. Roach III, P. Faillace-Akazawa, and C. Malfatti, "Prostate volumes and organ movement defined by serial computerized tomographic scans during three-dimensional conformal radiotherapy," Radiat. Oncol. Investig., 5(4): 187-194 (1997).
[6] T.R. Mackie, J. Kapatoes, K. Ruchala, W. Lu, C. Wu, G. Olivera, L. Forrest, W. Tome, J. Welsh, R. Jeraj, P. Harari, P. Reckwerdt, B. Paliwal, M. Ritter, H. Keller, J. Fowler, M. Mehta, "Image guidance for precise conformal radio-therapy," Int. J. Radiat. Oncol. Biol. Phys., 56(1):89-105 (2003).
[7] M. Berger, G. Gerig, “Motion measurements in low-contrast X-ray imagery,” Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention, 1496:832-841 (1998).
[8] K.G. Gilhuijs, P.J. van de Ven, M. van Herk, “Automatic three-dimensional inspection of patient setup in radiation therapy using portal images, simulator images, and computed tomography data,” Med. Phys., 23(3):389-399 (1996).
[9] P. Viola, W.M. Wells, “Alignment by maximization of mutual information,” Int J Comp Vision, 24(2):137-154 (1997).
[10] R. Bansal, L. Staib, Z. Chen, A. Rangarajan, J. Knisely, R. Nath, J. Duncan, “A Minimax entropy registration framework for patient setup verification in radiotherapy,” Computer Aided Surgery, 4(6):287-304 (1999).
[11] R. Bansal, L. Staib, Z. Chen, A. Rangarajan, J. Knisely, R. Nath, J. Duncan, “Entropy-based, dual-portal-to-3DCT registration incorporating pixel correlation,” IEEE Trans Med Imaging, 22(1):29-49 (2003).
[12] S. Chelikani, K. Purushothaman, J Knisely, Z. Chen, R. Nath, R. Bansal, and J. Duncan, "A gradient feature weighted minimax algorithm for registration of multiple portal images to 3DCT volumes in prostate radiotherapy," Int. J. Radiation Oncology Biol. Phys., 65(2):535-547 (2006).
[13] L.E. Court, L. Dong, “Automatic registration of the pros-tate for computed-tomography-guided radiotherapy,” Medical Physics, 30(10):2750-2757 (2003).
[14] Y. Chi, J. Liang, and D. Yan, “A material sensitivity study on the accuracy of deformable organ registration using linear biomechanical modelsa”, Med Phys., 33(2):421-433 (2006)
[15] R. Alterovitz, K. Goldberg, J. Pouliot, I.C. Hsu, Y. Kim, S.M. Noworolski, and J. Kurhanewicz, “Registration of MR prostate images with biomechanical modeling and nonlinear parameter estimation”, Medical physics, 33(2):446-54 (2006)
[16] D. Yan, D.A. Jaffray, J.W. Wong, “A model to accumulate fractionated dose in a deforming organ,” Int. J. Radiat Oncol., Biol., Phys. 44(3):665-675 (1999)
[17] L. Xiong, S. Haker, C. M. Tempany, L. M. Chin, and R. A. Cormack, “Deformable structure registration of bladder through surface mapping,” Med. Phys., 33:1848–1856 (2006)
[18] E.M. Vásquez Osorio, M.S. Hoogeman, L. Bondar, P.C. Levendag, B.J. Heijmen, “A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy”, Med Phys. 36(7):2848-59 (2009).
[19] N. Venugopal, B. McCurdy, A. Hnatov, and A. Dubey, “A feasibility study to investigate the use of thin-plate splines to account for prostate deformation,” Phys. Med. Biol. 50:2871-2885 (2005).
[20] W. Song, E. Wong, G. Bauman, J. Battista, and J. Van Dyk, “Dosimetric evaluation of daily rigid and nonrigid geometric correction strategies during on-line image-guided radiation therapy _IGRT_ of prostate cancer,” Med. Phys. 34:352-365, (2007).
[21] Godley A, Ahunbay E, Peng C, Li XA. “Automated registration of large deformations for adaptive radiation therapy of prostate cancer”, Med Phys. 36(4):1433-41 (2009)
[22] D. Yang, S.R. Chaudhari, S.M. Goddu, D. Pratt, D. Khullar, J.O. Deasy, I. El Naqa, “Deformable registration of abdominal kilovoltage treatment planning CT and tomotherapy daily megavoltage CT for treatment adaptation”, Med Phys. 36(2):329-38 (2009).
[23] X. Huang, N. Paragios, D. Metaxas, “Shape registration in implicit spaces using information theory and free form deformations”, IEEE Trans. Pattern Anal. Mach. Intell., 28(8):1302-1318, 2006
[24] W.H. Greene, S. Chelikani, X. Papademetris, J.P. Knisely, J. Duncan, “A Constrained Non-Rigid Registration Algorithm for Application in Prostate Radiotherapy,” IEEE International Symposium on Biomedical Imaging, 740-743 (2007).
[25] D. Paquin, D. Levy, L. Xing, “Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapy”, Med. Phys. 36(1):4-11 (2009)
[26] Jinghao Zhou, Sung Kim, Salma Jabbour, Sharad Goyal, Bruce Haffty, Ting Chen, Lydia Levinson, Dimitris Metaxas, Ning J. Yue, “A Deformable Model-based 3D Registration Algorithm for Image Guided Prostate Radiotherapy”, Medical Physics, Medical Physics, 37(3): 1298-1308, 2010.
[27] Olga Sorkine, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, Christian Rossl, and Hans-Peter Seidel, “Laplacian surface editing,” Proceedings of the 2004 Eurograph-ics/ACM SIGGRAPH symposium on Geometry processing, pp. 175-184, 2004.
[28] Andrew Nealen, Takeo Igarashi, Olga Sorkine, and Marc Alexa, “Laplacian mesh optimization,” GRAPHITE: Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia, pp. 381-389, ACM, 2006.

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