  Radio-frequency dosimetry is an important process in human safety and for
compliance of related products. Recently, computational human models generated
from medical images have often been used for such assessment, especially to
consider the inter-variability of subjects. However, the common procedure to
develop personalized models is time consuming because it involves excessive
segmentation of several components that represent different biological tissues,
which limits the inter-variability assessment of radiation safety based on
personalized dosimetry. Deep learning methods have been shown to be a powerful
approach for pattern recognition and signal analysis. Convolutional neural
networks with deep architecture are proven robust for feature extraction and
image mapping in several biomedical applications. In this study, we develop a
learning-based approach for fast and accurate estimation of the dielectric
properties and density of tissues directly from magnetic resonance images in a
single shot. The smooth distribution of the dielectric properties in head
models, which is realized using a process without tissue segmentation, improves
the smoothness of the specific absorption rate (SAR) distribution compared with
that in the commonly used procedure. The estimated SAR distributions, as well
as that averaged over 10-g of tissue in a cubic shape, are found to be highly
consistent with those computed using the conventional methods that employ
segmentation.
