Abstract
Human respiration hinders real-time accurate radiation in robotic radiosurgery with most of the extracranial tumors. Respiratory tumor motion tracking is crucial for respiration compensation in radiosurgery treatments. This paper presents our work on correlating the surrogate motion with the tumor motion both caused by human respiration. Usual correlation models between surrogates and tumor motions are built by linear or polynomial fitting based on least square method. In those models, sensor data are regarded as accurate. Our work aims to solve the respiration tracking problem by Kalman filters. In this paper, sensor data along with noises are considered in correlation model . Moreover, uncertainty of the model itself is obtained by calculating covariance of the model parameters induced using Unscented Transform.
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References
Berbeco RI, Nishioka S, Shirato H et al (2005) Residual motion of lung tumours in gated radiotherapy with external respiratory surrogates. Phys Med Biol 50:3655–3667
Ernst F (2011) Compensating for quasi-periodic motion in robotic radiosurgery. Springer. doi:10.1007/978-1-4614-1912-9
Ernst F, Martens V, Schlichting S et al (2009) Correlating chest surface motion to motion of the liver using epsilon-SVR–a porcine study. Med Image Comput Comput Assist Interv 12:356–364
Ernst F, Bruder R, Schlaefer A, Schweikard A (2012) Correlation between external and internal respiratory motion: a validation study. Int J Comput Assist Radiol Surg 7(3):483–492
Esmaili TA, Riboldi M, Imani Fooladi AA, Modarres Mosalla SM, Baroni G (2013) An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates. J Appl Clin Med Phys 14(1):102–114
Hoisak JD, Sixel KE, Tirona R, Cheung PC, Pignol JP (2004) Correlation of lung tumor motion with external surrogate indicators of respiration. Int J Radiat Oncol Biol Phys 60(4):1298–1306
Hoogeman M, Prévost JB, Nuyttens J, Pöll J, Levendag P, Heijmen B (2009) Clinical accuracy of the respiratory tumor tracking system of the cyberknife: assessment by analysis of log files. Int J Radiat Oncol Biol Phys 74(1):297–303
McClelland JR, Hughes S, Modat M, Qureshi A, Ahmad S, Landau DB, Ourselin S, Hawkes DJ (2011) Inter-fraction variations in respiratory motion models. Phys Med Biol 56:251–272
Rahni AA, Lewis E, Guy MJ, Goswami B, Wells K (2011) A particle filter approach to respiratory motion estimation in nuclear medicine imaging. IEEE Trans Nucl Sci 58(5):2276–2285
Sarrut D, Boldea V, Ayadi M et al (2005) Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer. Int J Radiat Oncol Biol Phys 61:594–607
Schweikard A (2000) Robotic motion compensation for respiratory movement during radiosurgery. Comput Aided Surg Off J Int Soc Comput Aided Surg 5(4):263–277
Seregni M, Pella A, Riboldi M, Orecchia R, Cerveri P, Baroni G (2013) Real-time tumor tracking with an artificial neural networks-based method: a feasibility study. Phys Med 29(1):48–59
Sharp GC, Jiang SB (2004) Predict ion of respiratory tumour motion for real time image guided radiotherapy. Phys Med Biol 49:425–440
Simon JJ, Jeffrey KU (1997) A new extension of the kalman filter to nonlinear systems. In: Proceedings of SPIE—the international society for optical engineering, pp 182–193
Urschel HC, John JK, James DL, Lech P, Robert DT, Raymond AS et al (2007) Treating tumors that move with respiration. Springer
Yvette S, Ross IB, Seiko N, Hiroki S, Ben H (2007) Accuracy of tumor motion compensation algorithm from a robotic respiratory tracking system: a simulation study. Med Phys 34(7):2774–2784
Acknowledgments
The authors would like to thank Dr. Floris Ernst from Institute for Robotics of Lubeck University. Dr. Ernst has been on the research of human respiratory motion signal for nearly ten years. The open dataset we used in this paper are found from https://signals.rob.uni-luebeck.de/index.php/Main_Page, on which Dr. Ernst and his colleagues share their data for scientific research.
Project supported by the National Natural Science Foundation of China (No. 61305108), Natural Science Foundation of Jiangsu Province (No. BK20130323), and High School Research foundation of Jiangsu Province (No. 13KJB520033).
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Yu, Sm., Zhang, Ff., Dou, M., Sun, Rc., Sun, Ln. (2017). Unscented Transform-Based Correlation Between Surrogate and Tumor Motion in Robotic Radiosurgery. In: Yang, C., Virk, G., Yang, H. (eds) Wearable Sensors and Robots. Lecture Notes in Electrical Engineering, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-10-2404-7_19
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DOI: https://doi.org/10.1007/978-981-10-2404-7_19
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