SAR Prediction in Human Head Tissues with Varying Material Parameters Using an Artificial Neural Network Proceedings Article
Nowadays, machine learning (ML) techniques are becoming popular in bio-electromagnetics. These techniques increase the capability of learning more experience and having more interpretations without explicit programming. In this contribution, artificial neural network (ANN) as a ML method is used to predict the specific absorption rate (SAR) in human head tissues versus parameter variation, during a plane wave field exposure. The dielectric properties of biological tissues are investigated for decades with an uncertainty index in provided values. As reported in literature, biological tissues show variability in dielectric properties up to ± 25% below 100 MHz. Efforts to quantify SAR values, considering tissues properties variation, mostly rely on full-wave simulations with extensive computational resources. An ANN framework is synthesized to facilitate having more results of SAR values in comparison to standard levels, over the tissue dielectric properties uncertainty. The inputs are dielectric properties (s and e) within ±20% variation from nominal values in a specific frequency (13.56 MHz) and local SAR values on each tissue are outputs. The framework is implemented and validated with various datasets of up to 500 full-wave simulation results for four different human head models from spherical to realistic ones. The results indicate up to ±6% and ±12% change on SAR values over ±20% variation of permittivity and conductivity properties respectively. The average accuracy on local SAR prediction by a three layer ANN is about 98% for various human head models within a few minutes implementation and calculation time in comparison to time and memory consuming full wave simulations.
620: Engineering and Applied Operations