Correlation between external and internal respiratory motion: a validation study

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



In motion-compensated image-guided radiotherapy, accurate tracking of the target region is required. This tracking process includes building a correlation model between external surrogate motion and the motion of the target region. A novel correlation method is presented and compared with the commonly used polynomial model.

Methods and Materials

The CyberKnife system (Accuray, Inc., Sunnyvale/CA) uses a polynomial correlation model to relate externally measured surrogate data (optical fibres on the patient’s chest emitting red light) to infrequently acquired internal measurements (X-ray data). A new correlation algorithm based on \({\varepsilon}\) -Support Vector Regression (SVR) was developed. Validation and comparison testing were done with human volunteers using live 3D ultrasound and externally measured infrared light-emitting diodes (IR LEDs). Seven data sets (5:03–6:27 min long) were recorded from six volunteers.


Polynomial correlation algorithms were compared to the SVR-based algorithm demonstrating an average increase in root mean square (RMS) accuracy of 21.3% (0.4 mm). For three signals, the increase was more than 29% and for one signal as much as 45.6% (corresponding to more than 1.5 mm RMS). Further analysis showed the improvement to be statistically significant.


The new SVR-based correlation method outperforms traditional polynomial correlation methods for motion tracking. This method is suitable for clinical implementation and may improve the overall accuracy of targeted radiotherapy.


Radiosurgery Correlation Respiratory motion Ultrasound 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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. British J Radiol 77: 588–596. doi: 10.1259/bjr/64800801 CrossRefGoogle Scholar
  2. 2.
    Berbeco RI, Jiang SB, Sharp GC, Chen GTY, Mostafavi H, Shirato H (2004) Integrated radiotherapy imaging system (IRIS): design considerations of tumour tracking with linac gantry-mounted diagnostic X-ray systems with flat-panel detectors. Phys Med Biol 49(2): 243–255. doi: 10.1088/0031-9155/49/2/005 PubMedCrossRefGoogle Scholar
  3. 3.
    Brown LG (1992) A survey of image registration techniques. ACM Comput Surv 24(4): 325–376. doi: 10.1145/146370.146374 CrossRefGoogle Scholar
  4. 4.
    Bruder R, Ernst F, Schweikard A (2011) A framework for real-time target tracking in radiosurgery using three-dimensional ultrasound. Int J Comput Assist Radiol Surg (Submitted)Google Scholar
  5. 5.
    Cho BC, Suh Y, Dieterich S, Keall PJ (2008) A monoscopic method for real-time tumour tracking using combined occasional X-ray imaging and continuous respiratory monitoring. Phys Med Biol 53(11): 2837–2855. doi: 10.1088/0031-9155/53/11/006 PubMedCrossRefGoogle Scholar
  6. 6.
    Ernst F, Bruder R, Schlaefer A, Schweikard A (2010) Improving the quality of biomedical signal tracking using prediction algorithms. In: Proceedings of the UKACC international conference on CONTROL 2010, vol 8. United Kingdom Automatic Control Council, Coventry, UK, pp 301–305Google Scholar
  7. 7.
    Ernst F, Martens V, Schlichting S, Beširević A, Kleemann M, Koch C, Petersen D, Schweikard A (2009) Correlating chest surface motion to motion of the liver using \({\varepsilon}\) -SVR—a porcine study. In: Yang GZ, Hawkes DJ, Rueckert D, Noble A, Taylor C (eds) MICCAI 2009, part II, lecture notes in computer science, vol 5762. MICCAI, Springer, London, pp 356–364. doi: 10.1007/978-3-642-04271-3_44.
  8. 8.
    George R, Vedam SS, Chung TD, Ramakrishnan V, Keall PJ (2005) The application of the sinusoidal model to lung cancer patient respiratory motion. Med Phys 32(9): 2850–2861. doi: 10.1118/1.2001220 PubMedCrossRefGoogle Scholar
  9. 9.
    Gierga DP, Brewer J, Sharp GC, Betke M, Willett CG, Chen GTY (2005) The correlation between internal and external markers for abdominal tumors: implications for respiratory gating. Int J Radiat Oncol Biol Phys 61(5): 1551–1558. doi: 10.1016/j.ijrobp.2004.12.013 PubMedCrossRefGoogle Scholar
  10. 10.
    Hoisak JDP, Sixel KE, Tirona R, Cheung PCF, Pignol JP (2004) Correlation of lung tumor motion with external surrogate indicators of respiration. Int J Radiat Oncol Biol Phys 60(4): 1298–1306. doi: 10.1016/j.ijrobp.2004.07.681 PubMedCrossRefGoogle Scholar
  11. 11.
    Kanoulas E, Aslam JA, Sharp GC, Berbeco RI, Nishioka S, Shirato H, Jiang SB (2007) Derivation of the tumor position from external respiratory surrogates with periodical updating of the internal/external correlation. Phys Med Biol 52(17): 5443–5456. doi: 10.1088/0031-9155/52/17/023 PubMedCrossRefGoogle Scholar
  12. 12.
    Khamene A, Warzelhan JK, Vogt S, Elgort D, Chefd’Hotel C, Duerk JL, Lewin J, Wacker FK, Sauer F (2004) Characterization of internal organ motion using skin marker positions. In: Barillot C, Haynor DR, Hellier P (eds) MICCAI 2004, Part II, LNCS, vol 3217. MICCAI, Springer, St. Malo, France, pp 526–533.
  13. 13.
    Koch N, Liu HH, Starkschall G, Jacobson M, Forster KM, Liao Z, Komaki R, Stevens CW (2004) Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: Part I—correlating internal lung motion with skin fiducial motion. Int J Radiat Oncol Biol Phys 60(5): 1459–1472. doi: 10.1016/j.ijrobp.2004.05.055 PubMedCrossRefGoogle Scholar
  14. 14.
    Kuglin CD, Hines DC (1975) The phase correlation image alignment method. In: Proceedings of the international conference on cybernetics and society, vol 1. IEEE Systems, Man, and Cybernetics Society, San Francisco, CA, USA, pp 163–165Google Scholar
  15. 15.
    Langner UW, Keall PJ (2009) Accuracy in the localization of thoracic and abdominal tumors using respiratory displacement, velocity, and phase. Med Phys 36(2): 386–393. doi: 10.1118/1.3049595 PubMedCrossRefGoogle Scholar
  16. 16.
    Low DA, Parikh PJ, Lu W, Dempsey JF, Wahab SH, Hubenschmidt JP, Nystrom MM, Handoko M, Bradley JD (2005) Novel breathing motion model for radiotherapy. Int J Radiat Oncol Biol Phys 63(3): 921–929. doi: 10.1016/j.ijrobp.2005.03.070 PubMedCrossRefGoogle Scholar
  17. 17.
    McClelland JR, Blackall JM, Tarte S, Chandler AC, Hughes S, Ahmad S, Landau DB, Hawkes DJ (2006) A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Med Phys 33(9): 3348–3358. doi: 10.1118/1.2222079 PubMedCrossRefGoogle Scholar
  18. 18.
    Murphy MJ, Isaaksson M, Jaldén J: Adaptive filtering to predict lung tumor breathing motion during imageguided radiation therapy. In: Proceedings of the 16th international conference and exhibition on computer assisted radiology and surgery (CARS’02), vol 16. Paris, France, pp 539–544.
  19. 19.
    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 15–30. doi: 10.1007/978-3-540-69886-9
  20. 20.
    Schweikard A, Glosser G, Bodduluri M, Murphy MJ, Adler JR Jr (2000) Robotic motion compensation for respiratory movement during radiosurgery. J Comput-Aided Surg 5(4): 263–277. doi: 10.3109/10929080009148894 CrossRefGoogle Scholar
  21. 21.
    Schweikard A, Shiomi H, Adler JR Jr (2004) Respiration tracking in radiosurgery. Med Phys 31(10): 2738–2741. doi: 10.1118/1.1774132 PubMedCrossRefGoogle Scholar
  22. 22.
    Shirato H, Shimizu S, Kitamura K, Nishioka T, Kagei K, Hashimoto S, Aoyama H, Kunieda T, Shinohara N, Dosaka-Akita H, Miyasaka K (2000) Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. Int J Radiat Oncol Biol Phys 48(2): 435–442. doi: 10.1016/s0360-3016(00)00625-8 PubMedCrossRefGoogle Scholar
  23. 23.
    Vedam SS, Kini VR, Keall PJ, Ramakrishnan V, Mostafavi H, Mohan R (2003) Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker. Med Phys 30(4): 505–513. doi: 10.1118/1.1558675 PubMedCrossRefGoogle Scholar
  24. 24.
    West JB (2008) Respiratory physiology: the essentials, 8th edn. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  25. 25.
    Yan H, Yin FF, Zhu GP, Ajlouni M, Kim JH (2006) Adaptive prediction of internal target motion using external marker motion: a technical study. Phys Med Biol 51(1): 31–44. doi: 10.1088/0031-9155/51/1/003 PubMedCrossRefGoogle Scholar

Copyright information

© CARS 2011

Authors and Affiliations

  • Floris Ernst
    • 1
  • Ralf Bruder
    • 1
  • Alexander Schlaefer
    • 2
  • Achim Schweikard
    • 1
  1. 1.Institute for Robotics and Cognitive SystemsUniversity of LübeckLübeckGermany
  2. 2.Medical Robotics GroupUniversity of LübeckLübeckGermany

Personalised recommendations