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Forecasting respiratory motion with accurate online support vector regression (SVRpred)

International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Object

To accurately deliver radiation in image-guided robotic radiosurgery, highly precise prediction algorithms are required. A new prediction method is presented and evaluated.

Materials and methods

SVRpred, a new prediction method based on support vector regression (SVR), has been developed and tested. Computer-generated data mimicking human respiratory motion with a prediction horizon of 150 ms was used for lab tests. The algorithm was subsequently evaluated on a respiratory motion signal recorded during actual radiosurgical treatment using the CyberKnife®. The algorithm’s performance was compared to the MULIN prediction methods and Wavelet-based multi scale autoregression (wLMS).

Results

The SVRpred algorithm clearly outperformed both the MULIN and the wLMS algorithms on both real (by 15 and 16 percentage points, respectively) and noise-corrupted simulated data (by 13 and 48 percentage points, respectively). Only on noise-free artificial data, the SVRpred algorithm did perform as well as the MULIN algorithms but not as well as the wLMS algorithm.

Conclusion

This new algorithm is a feasible tool for the prediction of human respiratory motion signals significantly outperforming previous algorithms. The only drawback is the high computational complexity and the resulting slow prediction speed. High performance computers will be needed to use the algorithm in live prediction of signals sampled at a high resolution.

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References

  1. Adler JR, Schweikard A, Murphy MJ, Hancock SL (1998) Image-guided stereo-tactic radiosurgery: the CyberKnife. In: Barnett G, Roberts D, Maciunas RJ (eds) Image-guided neurosurgery: clinical applications of surgical navigation. Quality Medical Publishing, St. Louis, pp 193–204

    Google Scholar 

  2. Schweikard A, Glosser G, Bodduluri M, Murphy MJ, Adler JR (2000) Robotic motion compensation for respiratory motion during radiosurgery. J Comput Aided Surg 5(4): 263–277

    Article  CAS  Google Scholar 

  3. Urschel HC Jr., Kresl JJ, Luketich JD, Papiez L, Timmerman RD (2007). Robotic radiosurgery. Treating tumors that move with respiration, 1st edn. Springer, Berlin

    Google Scholar 

  4. Ahn S, Yi B, Suh Y, Kim J, Lee S, Shin S, Choi E (2004) A feasibility study on the prediction of tumour location in the lung from skin motion. Br J Radiol 77: 588–596

    Article  PubMed  CAS  Google Scholar 

  5. Hoisak JDP, Sixel KE, Tirona R, Cheung PCF, Pignol J-P (2006) Prediction of lung tumour position based on spirometry and on abdominal displacement: accuracy and reproducibility. Radiother Oncol 78(3): 339–346

    Article  PubMed  Google Scholar 

  6. Kakar M, Nyström H, Aarup LR, Nøttrup TJ, Olsen DR (2005) Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). Phys Med Biol 50: 4721–4728

    Article  PubMed  Google Scholar 

  7. Murphy MJ, Dieterich S (2006) Comparative performance of linear and nonlinear neural networks to predict irregular breathing. Phys Med Biol 51(22): 5903–5914

    Article  PubMed  Google Scholar 

  8. Sharp GC, Jiang SB, Shimizu S, Shirato H (2004) Prediction of respiratory tumour motion for real-time image-guided radiotherapy. Phys Med Biol 49(3): 425–440

    Article  PubMed  Google Scholar 

  9. Ablitt N, Gao J, Keegan J, Stegger L, Firmin DN, Yang G-Z (2004) Predictive cardiac motion modeling and correction with PLSR. IEEE Trans Med Imaging 23(10):1315–1324. http://pubs.doc.ic.ac.uk/cardiac-modeling-correction/

    Google Scholar 

  10. Ernst F, Schlaefer A, Schweikard A (2007) Prediction of respiratory motion with wavelet-based multiscale autoregression. In: Ayache N, Ourselin S, Maeder A (eds) MICCAI 2007, Part II, ser. Lecture notes in computer science, vol 4792. MICCAI, November 2007. Springer, Brisbane, Australia, pp 668–675. http://www.miccai2007.org

  11. Vedam SS, Keall PJ, Docef A, Todor DA, Kini VR, Mohan R (2004) Predicting respiratory motion for four-dimensional radiotherapy. Med Phys 31(8): 2274–2283

    Article  PubMed  CAS  Google Scholar 

  12. Ramrath L, Schlaefer A, Ernst F, Dieterich S, Schweikard A (2007) Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter. In: Proceedings of the 21st international conference and exhibition on computer assisted radiology and surgery (CARS’07), ser. International Journal of CARS, vol 2, supp. 1, June 2007. CARS, Berlin, Germany, pp 56–58. http://www.cars-int.org/

  13. Drucker H, Burges CJC, Kaufman L, Smola AJ, Vapnik V (1997) Support vector regression machines. In: Advances in neural information processing systems, ser. NIPS, vol 9. MIT Press, Cambridge, pp 155–161

  14. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14: 199–222

    Article  Google Scholar 

  15. Ma J, Theiler J, Perkins S (2003) Accurate on-line support vector regression. Neural Comput 15(11):2683–2703. http://www.mitpressjournals.org/doi/abs/10.1162/089976603322385117

    Google Scholar 

  16. Ernst F, Schweikard A (2008) Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN). Int J Comput Assist Radiol Surg 3(1–2): 85–90

    Article  Google Scholar 

  17. Verleysen M, François D (2005) The curse of dimensionality in data mining and time series prediction. In: Proceedings of the 8th international workshop on artificial neural networks (IWANN 2005), ser. Lecture notes in computer science, vol 3512, June 2005. Springer, Barcelona, Spain, pp 758–770

  18. Parrella F (2007) Online support vector regression. Master’s Thesis, University of Genoa. Available online at http://onlinesvr.altervista.org/

  19. Burges CJC (1999) Geometry and invariance in kernel based methods. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 89–116

    Google Scholar 

  20. Knöpke M, Ernst F (2008) Flexible Markergeometrien zur Erfassung von Atmungs- und Herzbewegungen an der Körperoberfläche. In: 7. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie, 24–26 September 2008. CURAC, Leipzig, Germany, pp 15–16

  21. Demartines P, Herault J (1997) Curvilinear component analysis: a self-organizing neural networkfor nonlinear mapping of data sets. IEEE Trans Neural Netw 8(1): 148–154

    Article  PubMed  CAS  Google Scholar 

  22. Lee JA, Lendasse A, Verleysen M (2002) Curvilinear distance analysis versus isomap. In: European symposium on artificial neural networks (ESANN), 24–26 April 2002. Bruges, Belgium, pp 185–192

  23. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290: 2319–2323

    Article  PubMed  CAS  Google Scholar 

  24. Renaud O, Starck J-L, Murtagh F (2003) Prediction based on a multiscale decomposition. Int J Wavelets Multiresolut Inf Process 1(2): 217–232

    Article  Google Scholar 

  25. Rzezovski N, Ernst F (2008) Graphical tool for the prediction of respiratory motion signals. In: 7. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie, 24–26 September 2008. CURAC, Leipzig, Germany, pp 179–180

  26. Ernst F, Bruder R, Schlaefer A (2007) Processing of respiratory signals from tracking systems for motion compensated IGRT. In: 49th annual meeting of the AAPM, ser. Medical physics, vol 34, no 6, July 2007. American Association of Physicists in Medicine, Minneapolis-St. Paul, MN, USA, p 2565. http://www.aapm.org/meetings/07AM/

  27. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

  28. Sayeh S, Wang J, Main WT, Kilby W, Maurer CR (2007) Respiratory motion tracking for robotic radiosurgery. In: Jr Urschel HC, Kresl JJ, Luketich JD, Papiez L, Timmerman RD (eds) Robotic radiosurgery. Treating tumors that move with respiration, 1st edn. Springer, Berlin, pp 15–30

    Google Scholar 

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Correspondence to Floris Ernst.

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Ernst, F., Schweikard, A. Forecasting respiratory motion with accurate online support vector regression (SVRpred). Int J CARS 4, 439–447 (2009). https://doi.org/10.1007/s11548-009-0355-5

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  • DOI: https://doi.org/10.1007/s11548-009-0355-5

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