Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy

The treatment times of laser induced thermal therapies (LITT) guided by computational prediction are determined by the convergence behavior of PDE constrained optimization problems. In this work, we investigate the convergence behavior of a bioheat transfer constrained calibration problem to assess the feasibility of applying to real-time patient specific data. The calibration techniques utilize multi-planar thermal images obtained from the non-destructive in vivo heating of canine prostate. The calibration techniques attempt to adaptively recover the thermal heterogeneities within the tissue on a patient specific level and results in a formidable PDE constrained optimization problem to be solved in real time. Initial results presented here indicate that these calibration problems involving the inverse solution of thousands of model parameters can converge to a solution within three minutes and decrease the ‖ · ‖2L2(0,T;L2(Ω)) norm of the difference between computational prediction and the measured temperature values to a patient specific regime.