Forecasting pulsatory motion for non-invasive cardiac radiosurgery: an analysis of algorithms from respiratory motion prediction

  • Floris Ernst
  • Ralf Bruder
  • Alexander Schlaefer
  • Achim Schweikard
Original Article



Recently, radiosurgical treatment of cardiac arrhythmia, especially atrial fibrillation, has been proposed. Using the CyberKnife, focussed radiation will be used to create ablation lines on the beating heart to block unwanted electrical activity. Since this procedure requires high accuracy, the inevitable latency of the system (i.e., the robotic manipulator following the motion of the heart) has to be compensated for.

Materials and methods

We examine the applicability of prediction algorithms developed for respiratory motion prediction to the prediction of pulsatory motion. We evaluated the MULIN, nLMS, wLMS, SVRpred and EKF algorithms. The test data used has been recorded using external infrared position sensors, 3D ultrasound and the NavX catheter systems.


With this data, we have shown that the error from latency can be reduced by at least 10 and as much as 75% (44% average), depending on the type of signal. It has also been shown that, although the SVRpred algorithm was successful in most cases, it was outperformed by the simple nLMS algorithm, the EKF or the wLMS algorithm in a number of cases.


We have shown that prediction of cardiac motion is possible and that the algorithms known from respiratory motion prediction are applicable. Since pulsation is more regular than respiration, more research will have to be done to improve frequency-tracking algorithms, like the EKF method, which performed better than expected from their behaviour on respiratory motion traces.


Pulsatory motion Prediction Motion compensation Cardiac arrhythmia 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bruder R, Ernst F, Schlaefer A, Schweikard A (2009) Real-time tracking of the pulmonary veins in 3D ultrasound of the beating heart. In: 51st Annual Meeting of the AAPM. Medical Physics 36:2804. American Association of Physicists in Medicine, Anaheim, CA, USA. doi: 10.1118/1.3182643. URL TH-C-304A-07
  2. 2.
    CURAC (2008) Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie 7Google Scholar
  3. 3.
    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, Lecture Notes in Computer Science 4792:668–675. MICCAI, Springer, Brisbane, Australia. doi: 10.1007/978-3-540-75759-7_81. URL
  4. 4.
    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. doi: 10.1007/s11548-008-0211-z CrossRefGoogle Scholar
  5. 5.
    Ernst F, Schweikard A (2009) Forecasting respiratory motion with accurate online support vector regression (SVRpred). Int J Comput Assist Radiol Surg 4(5): 439–447. doi: 10.1007/s11548-009-0355-5 CrossRefPubMedGoogle Scholar
  6. 6.
    Ernst F, Schweikard A (2009) A survey of algorithms for respiratory motion prediction in robotic radiosurgery. In: Fischer S, Maehle E (eds) 39. GI Jahrestagung, Lecture Notes in Informatics, 154. GI, Bonner Köllen, Lübeck, Germany, pp 1035–1043. URL
  7. 7.
    Haykin S (2002) Adaptive Filter Theory. 4th edn. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  8. 8.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82: 35–45Google Scholar
  9. 9.
    Kalman RE, Bucy RS (1961) New results in linear filtering and prediction theory. Trans ASME J Basic Eng 83: 95–108Google Scholar
  10. 10.
    Knöpke M, Ernst F, Flexible Markergeometrien zur Erfassung von Atmungs- und Herzbewegungen an der Körperoberfläche. In: 7. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie [2], pp 15–16Google Scholar
  11. 11.
    Maguire P, Sharma A, Fogarty T, Sumanaweera T, Jack A (2008) Non-invasive radiosurgical ablation of the myocardium: Pre clinical electrophysiology and histology. In: Boston Atrial Fibrillation SymposiumGoogle Scholar
  12. 12.
    Mewis C, Neuberger HR, Böhm M (2006) Vorhoffflimmern. Dtsch Med Wochenschr 131(50): 2843–2854. doi: 10.1055/s-2006-957212 CrossRefPubMedGoogle Scholar
  13. 13.
    Parrella F (2007) Online support vector regression. Master’s thesis, University of GenoaGoogle Scholar
  14. 14.
    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), Int J Comput Assist Radiol Surg 2(S1):56–58. CARS, Berlin, Germany. doi: 10.1007/s11548-007-0083-7. URL
  15. 15.
    Renaud O, Starck JL, Murtagh F (2005) Wavelet-based combined signal filtering and prediction. IEEE Trans Syst Man Cybern B Cybern 35(6): 1241–1251CrossRefPubMedGoogle Scholar
  16. 16.
    Rzezovski N, Ernst F, Graphical tool for the prediction of respiratory motion signals. In: 7. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie [2], pp 179–180Google Scholar
  17. 17.
    Sayeh S, Wang J, Main WT, Kilby W, Maurer CR Jr (2007) Robotic Radiosurgery. Treating Tumors that Move with Respiration, 1st edn., chap. Respiratory motion tracking for robotic radiosurgery. Springer, Berlin, pp 15–30Google Scholar
  18. 18.
    Schmidt, RF, Lang, F, Thews, G (eds) (2005) Physiologie des Menschen, 29th edn. Springer Lehrbuch. Springer, Berlin, Heidelberg, New YorkGoogle Scholar
  19. 19.
    Sharma A, Maguire P, Fajardo L, Wong D, Sumanaweera T, Fogarty T (2008) Non-invasive approach to myocardial ablation: Pathology of stereotactic robot targeted high energy x-ray lesions at potential arrhythmia sites. In: 2008 Heart Rhythm Symposium, Heart Rhythm 5:S67Google Scholar
  20. 20.
    Sharma A, Maguire P, Sumanaweera T, Wong D, Marshall R, Fajardo L, Fogarty T (2007) Non-invasive ablation of the left superior pulmonary vein-left atrial junction using stereotactic focussed radiation. Circulation 116:II 489 URL
  21. 21.
    Sharma A, Maguire P, Wong D, Sumanaweera T, Steele J, Peterson P, Fajardo L, Takeda P, Fogarty T (2007) New non-invasive therapy for cardiac arrhythmias using stereotactic radiosurgery: Initial feasibility testing. In: 2007 Heart Rhythm Symposium, Heart Rhythm 4:S68Google Scholar
  22. 22.
    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14: 199–222CrossRefGoogle Scholar
  23. 23.
    Yuen SG, Kettler DT, Novotny PM, Plowes RD, Howe RD (2009) Robotic motion compensation for beating heart intracardiac surgery. Int J Rob Res 28(10): 1355–1372. doi: 10.1177/0278364909104065 CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2010

Authors and Affiliations

  • Floris Ernst
    • 1
  • Ralf Bruder
    • 1
  • Alexander Schlaefer
    • 1
  • Achim Schweikard
    • 1
  1. 1.Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckGermany

Personalised recommendations