Skip to main content
SpringerLink
Log in

Learning Temperature Dynamics on Agar-Based Phantom Tissue Surface During Single Point CO\(_2\) Laser Exposure

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

This paper presents an approach to determine a model of superficial tissue temperature dynamics during continuous wave CO\(_2\) laser irradiation. The main contribution of this research is the use of real data to model the thermal state of targets being irradiated with surgical laser, using statistical learning methods. To the best of our knowledge this is the first time that these methods have been applied in this field. This work extends previous results which demonstrated, in simulation, the relevance and utility of machine learning methods in modeling the thermal state of tissue during laser surgical procedures. Here we use real data, captured with a thermal camera, to learn the function mapping from laser inputs to the corresponding changes in tissue temperature. Based on features observed in the real data, a new structure for the model as well as a different learning approach are proposed.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Steine W, Ambrosch P (2000) Endoscopic laser surgery of the upper aerodigestive tract: with special emphasis on cancer surgery. Thieme, Stuttgart

    Google Scholar 

  2. Lumenis inc., Co\(_2\) laser accesories. [Online]. http://www.surgical.lumenis.com/pdf/PB006035_B_CO2_Accessories.pdf. Accessed 17 Oct 2014

  3. uRalp Consortium [Online]. Micro-technologies and systems for robot-assisted laser phonomicrosurgery, EU project—grant agreement 288663. http://www.microralp.eu. Accessed 17 Oct 2014

  4. Mattos LS, Dellepiane M, Caldwell D (2011) Next-generation micromanipulator for computer-assisted laser phonomicrosurgery. In Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE, vol 30. 3 Sept 2011, pp 4555–4559.

  5. Mohri M, Rostamizadeh A, Talwalkar A (2012) Foundations of machine learning. MIT press, Cambridge

    Google Scholar 

  6. Niemz M (2003) Laser-tissue interactions. Springer, Berlin

    Google Scholar 

  7. Cox B (2014) Optics in medicine: introduction to laser-tissue interactions (Course Notes for MPHY3886). Downloaded in Fall from http://www.ucl.ac.uk/medphys/staff/people/bcox/BenCox_LaserTissueInteractions.pdf

  8. Steiner R (2011) Laser-tissue interactions. Laser and IPL technology in dermatology and aesthetic medicine. Springer, Berlin, pp 23–36

    Chapter  Google Scholar 

  9. Saccomandi P, Schena E, Silvestri S (2013) Techniques for temperature monitoring during laser-induced thermotherapy: an overview. Int J Hyperth 29(7):609–619

  10. Jiao J, Guo Z (2009) Thermal interaction of short-pulsed laser focused beams with skin tissues. Phys Med Biol 54(13):4225–4241

    Article  Google Scholar 

  11. Ahmed EM, Barrera FJ, Early EA, Denton ML, Clark CD, Sardar DK (2013) Maxwell’s equations-based dynamic laser tissue interaction model. Comput Biol Med 43(12):2278–2286

    Article  Google Scholar 

  12. Crochet JJ, Gnyawali SC, Chen Y, Lemley EC, Wang LV, Chen WR (2006) Temperature distribution in selective laser-tissue. J Biomed Opt 11(3):01–10

    Article  Google Scholar 

  13. Fichera L, Pardo D, Mattos L (2013) Modeling tissue temperature dynamics during laser exposure. Advances in computational intelligence—IWANN13, vol 7903. Springer, Berlin

    Google Scholar 

  14. Vijayakumar S, D’Souza A, Schaal S (2005) Incremental online learning in high dimensions. Neural Comput 17:2602–2634

    Article  Google Scholar 

  15. Vogel A, Venugopalan V (2003) Mechanisms of pulsed laser ablation of biological tissues. Chem Rev 103(2):577–644

    Article  Google Scholar 

  16. Svelto O (2010) Principles of lasers. Springer, New York

    Book  Google Scholar 

  17. Caruana R, Searle R, Heller T, Shupack S (1986) Fast algorithm for the resoluton of spectra. Anal Chem 58(6):1162–1167

    Article  Google Scholar 

  18. Guo H (2011) A simple algorithm for fitting a gaussian function. IEEE Signal Process Mag 28:134–137

    Article  Google Scholar 

  19. Rastegar S, van Gemert MJC, Welch AJ, Hayes LJ (1988) Laser ablation of discs of agar gel. Phys Med Biol 33:133

    Article  Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme FP7\(/\)2007–2013—Challenge 2—Cognitive Systems, Interaction, Robotics—under Grant agreement \(\mu \)RALP \(^{\circ }\) 288233.

Author information

Authors and Affiliations

  1. Department of Advanced Robotics, Istituto Italiano di Tecnologia, Via Morego, 30, 16163, Genoa, Italy

    Diego Pardo, Loris Fichera, Darwin Caldwell & Leonardo S. Mattos

Authors
  1. Diego Pardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Loris Fichera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Darwin Caldwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Leonardo S. Mattos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diego Pardo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pardo, D., Fichera, L., Caldwell, D. et al. Learning Temperature Dynamics on Agar-Based Phantom Tissue Surface During Single Point CO\(_2\) Laser Exposure. Neural Process Lett 42, 55–70 (2015). https://doi.org/10.1007/s11063-014-9389-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-014-9389-y

Keywords

Search

Navigation