Model-based real-time control for laser induced thermal therapy with applications to prostate cancer treatment

In this paper, we present a model-based predictive control system that is capable of capturing physical and biological variations of laser-tissue interaction as well as heterogeneity in real-time during laser induced thermal therapy (LITT). Using a three-dimensional predictive bioheat transfer model, which is built based on regular magnetic resonance imaging (MRI) anatomic scan and driven by imaging data produced by real-time magnetic resonance temperature imaging (MRTI), the computational system provides a regirous real-time predictive control during surgical operation process. The unique feature of the this system is its ability for predictive control based on validated model with high precision in real-time, which is made possible by implementation of efficient parallel algorithms. The major components of the current computational systems involves real-time finite element solution of the bioheat transfer induced by laser-tissue interaction, solution module of real-time calibration problem, optimal laser source control, goal-oriented error estimation applied to the bioheat transfer equation, and state-of-the-art imaging process module to characterize the heterogeneous biological domain. The system was tested in vivo in a canine animal model in which an interstitial laser probe was placed in the prostate region and the desired treatment outcome in terms of ablation temperature and damage zone were achieved. Using the guidance of the predictive model driven by real-time MRTI data while applying the optimized laser heat source has the potential to provide unprecedented control over the treatment outcome for laser ablation.