SU-F-J-03: Treatment Planning for Laser Ablation Therapy in Presence of Heterogeneous Tissue: A Retrospective Study




MR guided Laser Induced Thermal Therapy (MRgLITT) is an effective technique for cancerous tissue destruction in brain. Precise modeling of the thermal ablation procedure is important for treatment planning for MRgLITT. Physical properties of target tissue such as optical attenuation (µ) highly affect the outcome of thermal ablation modeling. Hence to ensure accuracy of modeling scheme, it is crucial to precisely estimate patient specific heterogeneity in these parameters.


A steady-state Bioheat model is used for simulation of temperature field over the heterogeneous target tissue. Physical properties are assessed from training based on previously recorded MRTI datasets. In detail, a cost function is defined based on the difference between the Bioheat model temperature predictions and MRTI measurements. Minimizing this cost function results in optimal values for physical properties of each tissue type. Optical attenuation coefficients (µ) are the dominant sensitivity and are optimized in this work. Nominal values of other parameters are used. Population averages of the optimal values of µ are then used in Bioheat model for temperature assessment in future patients. Performance of these predictions is then verified using Dice similarity index.


Results provide a comprehensive analysis of the effect of using heterogeneous tissue segmentations on performance of LITT mathematical simulation. Optimal values of µ coefficients range between 100 m−1 and 200 m−1. This corresponds with obtained values in literature. The mean and median of Dice similarity index while using optimal values of optical attenuation coefficients is 0.8022 and 0.8225, respectively.


Calibration of mathematical modeling outcomes with clinical MRTI data provides a better understanding about the parameters of the Bioheat equation and consequently provides a more confident thermal dose assessment than either approach alone. Results demonstrate that modeling predictions are accurate in a-priori prediction of thermal dose and may be useful in planning the procedure.