WE-C-116-10: Univariate, Multivariate, and Nonlinear Uncertainty Quantification for Magnetic Resonance-Guided Laser Induced Thermal Therapy

Abstract

Purpose: MR‐guided laser‐induced thermal therapy (MRgLITT) is an emerging minimally invasive neurosurgical tool being explored as a treatment alternative for conditions such as motion disorder, radiation necrosis, and intracranial metastases. The primary goal is to reduce complications and normal tissue morbidity associated with conventional surgery. Computational models are being investigated to aid prospective LITT planning; however, accuracy is undermined by imprecise and non‐patient specific knowledge of parameters. This work explores incorporating uncertainty quantification (UQ) of temperature output from the stochastic Pennes bioheat transfer equation (BHT). Methods: A five parameter (perfusion, thermal conductivity, optical absorption, optical scattering) stochastic BHT LITT model was used. Parameters were considered to be uniform distributions with ranges informed by literature values. Generalized polynomial chaos (gPC) was employed to calculate spatio‐temporal, voxel‐wise functions of the output temperature distributions for UQ. BHT parameter sensitivity in linear and nonlinear models was explored in silico using univariate gPC. Retrospective analysis of MR thermography (MRTI) from both phantom and MRgLITT in normal canine brain in vivo (n=4) was explored using multivariate gPC. Isotherms, temporal and linear profiles were reported. Results: Univariate simulations demonstrated that optical parameters explained the majority of model variance (peak standard deviation: anisotropy 3.75 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.42 °C, and perfusion 0.94 °C). Linear model variance enclosed nonlinear model variance. Mean temperature and 95% confidence interval from multivariate simulations correlated well with measured heating even near the applicator. Conclusion: gPC may provide robust and relatively fast UQ facilitating useful prospective LITT planning in brain tissue despite imprecise knowledge of parameters. The faster linear simulation approximated the nonlinear simulation without excessive variance. Further, the computational burden was reduced with minimal accuracy loss by including only the most sensitive parameters. Subsequent work includes applying stochastic BHT to retrospective human brain tumor LITT.