Stochastic Thermal Dosimetry for the Three Compartment Head Model

This paper presents a stochastic approach to the assessment of the temperature elevation in human head tissues due to the exposure to high frequency electromagnetic field. The novelty in this work is based on the coupling of the heterogeneous human head model with the stochastic method. Namely, the thermal parameters of three head tissues are modeled as random variables in order to capture the influence of the input uncertainty on the temperature elevation. The volumetric perfusion blood rate and tissue thermal conductivity of scalp, skull and brain are modelled as random variables with uniform distributions. The chosen thermal parameters are selected according to the findings in the previous work of the authors for a simpler homogeneous human brain model. The chosen parameters are shown to be the most influential ones regarding the temperature elevation. The propagation of uncertainties from the input random parameters to the output of interest, i.e. temperature elevation is carried out by using the non-intrusive Lagrange stochastic collocation method. A sparse grid interpolation in the multidimensional random space is used which speeds up the calculation compared to traditional Monte Carlo sampling methods or full tensor stochastic collocation approach. The presented results provide an insight into the behavior of the model output with respect to parameter variations and allows the ranking of the input parameters from the most to the least influential ones.