Development of a Computational Paradigm for Laser Treatment of Cancer

J.T. Oden1, K.R. Diller2, C. Bajaj3, J.C. Browne3, J. Hazle4, I. Babuška1, J. Bass1, L. Demkowicz1, Y. Feng1, D. Fuentes1, S. Prudhomme1, M.N. Rylander2, R.J. Stafford4, and Y. Zhang1

1 Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin TX 78712, USA
oden@ices.utexas.edu
babuska@ices.utexas.edu
bass@ices.utexas.edu
leszek@ices.utexas.edu
feng@ices.utexas.edu
fuentes@ices.utexas.edu
serge@ices.utexas.edu
jessica@ices.utexas.edu

2 Department of Biomedical Engineering, The University of Texas at Austin, Austin TX 78712, USA
kdiller@mail.utexas.edu
n.forney@mail.utexas.edu

3 Department of Computer Science, The University of Texas at Austin, Austin TX 78712, USA
bajaj@cs.utexas.edu
browne@cs.utexas.edu

4 University of Texas M.D. Anderson Cancer Center, Department of Diagnostic Radiology, Houston TX 77030, USA
jhazle@mdanderson.org
jstafford@mdanderson.org

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 im plemented involve uncertainty quantification methods designed to provide an innovative, data-driven, patient-specific approach to effective cancer treatment.

LNCS 3993, pp. 530-537.

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