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Annals of Biomedical Engineering

, Volume 37, Issue 4, pp 763–782 | Cite as

Computational Modeling and Real-Time Control of Patient-Specific Laser Treatment of Cancer

  • D. Fuentes
  • J. T. Oden
  • K. R. Diller
  • J. D. Hazle
  • A. Elliott
  • A. Shetty
  • R. J. Stafford
Article

Abstract

An adaptive feedback control system is presented which employs a computational model of bioheat transfer in living tissue to guide, in real-time, laser treatments of prostate cancer monitored by magnetic resonance thermal imaging. The system is built on what can be referred to as cyberinfrastructure—a complex structure of high-speed network, large-scale parallel computing devices, laser optics, imaging, visualizations, inverse-analysis algorithms, mesh generation, and control systems that guide laser therapy to optimally control the ablation of cancerous tissue. The computational system has been successfully tested on in vivo, canine prostate. Over the course of an 18 min laser-induced thermal therapy performed at M.D. Anderson Cancer Center (MDACC) in Houston, Texas, the computational models were calibrated to intra-operative real-time thermal imaging treatment data and the calibrated models controlled the bioheat transfer to within 5 °C of the predetermined treatment plan. The computational arena is in Austin, Texas and managed at the Institute for Computational Engineering and Sciences (ICES). The system is designed to control the bioheat transfer remotely while simultaneously providing real-time remote visualization of the on-going treatment. Post-operative histology of the canine prostate reveal that the damage region was within the targeted 1.2 cm diameter treatment objective.

Keywords

Hyperthermia Real-time computing Medical imaging Cancer treatment Cyberinfrastructure PDE constrained optimization 

Notes

Acknowledgments

The research in this paper was supported in part by the National Science Foundation under Grants CNS-0540033, IIS-0325550, and NIH Contracts P20RR0206475, GM074258. During the course of this work we benefited from advice and comments of many colleagues, we mention in particular, C. Bajaj, J. C. Browne, I. Babuška, J. Bass, L. Bidaut, L. Demkowicz, Y. Feng, S. Goswami, A. Hawkins, S. Khoshnevis, B. Kwon, and S. Prudhomme. The authors also acknowledge the important support of DDDAS research by Dr. Frederica Darema of NSF. The authors would like to thank Drs. Ashok Gowda and Roger McNichols from BioTex Inc. for providing the Visualase® System and altering the software so that it may be remotely controlled from Austin, TX.

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Copyright information

© Biomedical Engineering Society 2009

Authors and Affiliations

  • D. Fuentes
    • 1
  • J. T. Oden
    • 1
  • K. R. Diller
    • 2
  • J. D. Hazle
    • 3
  • A. Elliott
    • 3
  • A. Shetty
    • 3
  • R. J. Stafford
    • 3
  1. 1.Institute for Computational Engineering and SciencesThe University of Texas at AustinAustinUSA
  2. 2.Department of Biomedical EngineeringThe University of Texas at AustinAustinUSA
  3. 3.Department of Imaging PhysicsUniversity of Texas M.D. Anderson Cancer CenterHoustonUSA

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