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Development of a Computational Paradigm for Laser Treatment of Cancer

  • J. T. Oden
  • K. R. Diller
  • C. Bajaj
  • J. C. Browne
  • J. Hazle
  • I. Babuška
  • J. Bass
  • L. Demkowicz
  • Y. Feng
  • D. Fuentes
  • S. Prudhomme
  • M. N. Rylander
  • R. J. Stafford
  • Y. Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)

Abstract

The goal of this project is to develop a dynamic data-driven planning and control system for laser treatment of cancer. The research includes (1) development of a general mathematical framework and a family of mathematical and computational models of bio-heat transfer, tissue damage, and tumor viability, (2) dynamic calibration, verification and validation processes based on laboratory and clinical data and simulated response, and (3) design of effective thermo-therapeutic protocols using model predictions. At the core of the proposed systems is the adaptive-feedback control of mathematical and computational models based on a posteriori estimates of errors in key quantities of interest, and modern Magnetic Resonance Temperature Imaging (MRTI), and diode laser devices to monitor treatment of tumors in laboratory animals. This approach enables an automated systematic model selection process based on acceptance criteria determined a priori. The methodologies to be implemented involve uncertainty quantification methods designed to provide an innovative, data-driven, patient-specific approach to effective cancer treatment.

Keywords

Computational Paradigm Pennes Model Thermal Dose Local Error Estimate Dynamic Calibration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. T. Oden
    • 1
  • K. R. Diller
    • 2
  • C. Bajaj
    • 3
  • J. C. Browne
    • 3
  • J. Hazle
    • 4
  • I. Babuška
    • 1
  • J. Bass
    • 1
  • L. Demkowicz
    • 1
  • Y. Feng
    • 1
  • D. Fuentes
    • 1
  • S. Prudhomme
    • 1
  • M. N. Rylander
    • 2
  • R. J. Stafford
    • 4
  • Y. Zhang
    • 1
  1. 1.Institute for Computational Engineering and Sciences 
  2. 2.Department of Biomedical Engineering 
  3. 3.Department of Computer ScienceThe University of Texas at AustinAustinUSA
  4. 4.Department of Diagnostic RadiologyUniversity of Texas M.D. Anderson Cancer CenterHoustonUSA

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