Skip to main content
Log in

The internal–external respiratory motion correlation is unaffected by audiovisual biofeedback

  • Scientific Paper
  • Published:
Australasian Physical & Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

This study evaluated if an audiovisual (AV) biofeedback causes variation in the level of external and internal correlation due to its interactive intervention in natural breathing. The internal (diaphragm) and external (abdominal wall) respiratory motion signals of 15 healthy human subjects under AV biofeedback and free breathing (FB) were analyzed and measures of correlation and regularity taken. Regularity metrics (root mean square error and spectral power dispersion metric) were obtained and the correlation between these metrics and the internal and external correlation was investigated. For FB and AV biofeedback assisted breathing the mean correlations found between internal and external respiratory motion were 0.96 ± 0.02 and 0.96 ± 0.03, respectively. This means there is no evidence to suggest (p-value = 0.88) any difference in the correlation between internal and external respiratory motion with the use of AV biofeedback. Our results confirmed the hypothesis that the internal–external correlation with AV biofeedback is the same as for free breathing. Should this correlation be maintained for patients, AV biofeedback can be implemented in the clinic with confidence as regularity improvements using AV biofeedback with an external signal will be reflected in increased internal motion regularity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Keall PJ, Mageras GS, Balter JM, Emery RS, Forster KM, Jiang SB, Kapatoes JM, Low DA, Murphy MJ, Murray BR (2006) The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys 33:3874

    Article  PubMed  Google Scholar 

  2. Vedam S, Kini V, Keall P, Ramakrishnan V, Mostafavi H, Mohan R (2003) Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker. Med Phys 30:505

    Article  CAS  PubMed  Google Scholar 

  3. Wong JW, Sharpe MB, Jaffray DA, Kini VR, Robertson JM, Stromberg JS, Martinez AA (1999) The use of active breathing control (ABC) to reduce margin for breathing motion. Int J Radiat Oncol* Biol* Phys 44(4):911–919

    Article  CAS  Google Scholar 

  4. Marks LB, Bentzen SM, Deasy JO, Kong FMS, Bradley JD, Vogelius IS, El Naqa I, Hubbs JL, Lebesque JV, Timmerman RD (2010) Radiation dose–volume effects in the lung. Int J Radiat Oncol* Biol* Phys 76(3):S70–S76

    Article  Google Scholar 

  5. Kothary N, Heit JJ, Louie JD, Kuo WT, Loo BW, Koong A, Chang DT, Hovsepian D, Sze DY, Hofmann LV (2009) Safety and efficacy of percutaneous fiducial marker implantation for image-guided radiation therapy. J Vasc Interv Radiol 20(2):235–239

    Article  PubMed  Google Scholar 

  6. Venkat RB, Sawant A, Suh Y, George R, Keall PJ (2008) Development and preliminary evaluation of a prototype audiovisual biofeedback device incorporating a patient-specific guiding waveform. Phys Med Biol 53:N197

    Article  PubMed  Google Scholar 

  7. George R, Chung TD, Vedam SS, Ramakrishnan V, Mohan R, Weiss E, Keall PJ (2006) Audio–visual biofeedback for respiratory gated radiotherapy: impact of audio instruction and audio–visual biofeedback on respiratory-gated radiotherapy. Int J Radiat Oncol Biol 65(3):924–933

    Article  Google Scholar 

  8. Lu W, Neuner G, George R, Wang Z, Sasor S, Huang X, Regine W, Feigenberg S, D’Souza W (2014) Audio–visual biofeedback does not improve the reliability of target delineation using maximum intensity projection in 4-dimensional computed tomography radiation therapy planning. Int J Radiat Oncol Biol 88(1):229–235

    Article  Google Scholar 

  9. Kim T, Pollock S, Lee D, O’Brien R, Keall P (2012) Audiovisual biofeedback improves diaphragm motion reproducibility in MRI. Med Phys 39:6921

    Article  PubMed Central  PubMed  Google Scholar 

  10. Gierga D, Brewer J, Sharp G, Betke M, Willett C, Chen G (2005) The correlation between internal and external markers for abdominal tumors: implications for respiratory gating. Int J Radiat Oncol Biol 61(5):1551–1558

    Article  Google Scholar 

  11. Mageras G, Yorke E, Rosenzweig K, Braban L, Keatley E, Ford E, Leibel S, Ling C (2001) Fluoroscopic evaluation of diaphragmatic motion reduction with a respiratory gated radiotherapy system. Journal of Applied Clinical Medical Physics 2(4):191–200

    Article  CAS  PubMed  Google Scholar 

  12. Ozhasoglu C, Murphy M (2002) Issues in respiratory motion compensation during external-beam radiotherapy. Int J Radiat Oncol Biol 52(5):1389–1399

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  14. Fay MP, Proschan MA (2010) Wilcoxon–Mann–Whitney or t test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv 4:1–39

    Article  PubMed Central  PubMed  Google Scholar 

  15. Lim S, Park S, Do Ahn S, Suh Y, Shin S, Lee S-W, Kim J, Choi E, Yi B, Kwon S, Kim S, Jeung T (2007) Guiding curve based on the normal breathing as monitored by thermocouple for regular breathing. Med Phys 34(11):4514

    Article  PubMed  Google Scholar 

  16. Kini V, Vedam S, Keall P, Patil S, Chen C, Mohan R (2003) Patient training in respiratory-gated radiotherapy. Med Dosim 28(1):7–11

    Article  PubMed  Google Scholar 

  17. Cho B, Poulsen P, Ruan D, Sawant A, Keall P (2012) Experimental investigation of a general real-time 3D target localization method using sequential kV imaging combined with respiratory motion. Phys Med Biol 57:7395–7407

    Article  PubMed Central  PubMed  Google Scholar 

  18. Cervino LI, Chao AKY, Sandhu A, Jiang SB (2009) The diaphragm as an anatomic surrogate for lung tumor motion. Phys Med Biol 54:3529

    Article  PubMed  Google Scholar 

  19. Cerviño LI, Jiang Y, Sandhu A, Jiang SB (2010) Tumor motion prediction with the diaphragm as a surrogate: a feasibility study. Phys Med Biol 55:N221

    Article  PubMed  Google Scholar 

  20. Li R, Lewis JH, Berbeco RI, Xing L (2012) Real-time tumor motion estimation using respiratory surrogate via memory-based learning. Phys Med Biol 57:4771–4786

    Article  PubMed Central  PubMed  Google Scholar 

  21. Ruan D (2010) Kernel density estimation-based real-time prediction for respiratory motion. Phys Med Biol 55:1311

    Article  PubMed  Google Scholar 

  22. Murphy MJ, Balter J, Balter S, BenComo JA Jr, Das IJ, Jiang SB, Ma CM, Olivera GH, Rodebaugh RF, Ruchala KJ (2007) The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75. Med Phys 34:4041

    Article  PubMed  Google Scholar 

  23. Hoisak DJ, Sixel K, Tirona R, Cheung P, Pignol JP (2004) Correlation of lung tumor motion with external surrogate indicators of respiration. Radiat Oncol Biol Phys 60(4):1298–1306

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Sydney Medical School New Staff/Early Career Researcher Scheme grant, NIH/NCI R01CA93626 and an NHMRC Australia Fellowship.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Taeho Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Steel, H., Pollock, S., Lee, D. et al. The internal–external respiratory motion correlation is unaffected by audiovisual biofeedback. Australas Phys Eng Sci Med 37, 97–102 (2014). https://doi.org/10.1007/s13246-014-0247-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-014-0247-z

Keywords

Navigation