Induced Electric Field in Learning-Based Head Models With Smooth Conductivity for Exposure to Uniform Low-Frequency Magnetic Fields

Computational human models generated from medical images have been widely used to assess induced electric field for exposure to electromagnetic field. Traditional methods to develop human models include tissue segmentation, which involves huge effort in identifying tissues from medical images. When such models are applied to low-frequency electromagnetic dosimetry, computational artifacts result in substantial error. Deep learning techniques have been utilized to map medical images directly to tissue electrical conductivity, generating human models with smooth transitions in tissue conductivity across tissue boundaries and even within the same tissue. In this study, eight head models with smoothed conductivities were generated using the deep learning network. The induced electric fields in the models were assessed for exposure to a uniform low-frequency magnetic field and were compared with traditional segmented models. Computational results showed that the induced electric field distributions in learning-based and segmented models were consistent, and the former was smoother. The differences in the 99th to 99.99th percentile values between nonuniform and segmented models were within 8% and 13% for gray and white matter, respectively. The staircasing errors were suppressed in the learning-based models because of the smooth transition of the conductivity values, especially at the tissue interface. The intersubject variation of the maximum electric fields was smaller for the nonuniform models than for the segmented models, with a relative standard deviation within 12% for nonuniform models and 22% for segmented models. This difference is much smaller than the reduction factor of 3 associated with the numerical uncertainty set in the International Commission on Non-Ionizing Radiation Protection 2010 guidelines. Our findings could be helpful in deriving appropriate reduction factor in international guidelines, which is used for setting the limit from the threshold of adverse health effects.