Data from: Finite-element analysis of microwave scattering from a three-dimensional human head model for brain stroke detection

In this paper, a detailed analysis of microwave (MW) scattering from a
three-dimensional (3D) anthropomorphic human head model is presented. It
is the first time that the finite-element method (FEM) has been deployed
to study the MW scattering phenomenon of a 3D realistic head model for
brain stroke detection. A major contribution of this paper is to add
anatomically more realistic details to the human head model compared with
the literature available to date. Using the MRI database, a 3D numerical
head model was developed and segmented into 21 different types through a
novel tissue-mapping scheme and a mixed-model approach. The heterogeneous
and frequency-dispersive dielectric properties were assigned to brain
tissues using the same mapping technique. To mimic the simulation set-up,
an eight-elements antenna array around the head model was designed using
dipole antennae. Two types of brain stroke (haemorrhagic and ischaemic) at
various locations inside the head model were then analysed for possible
detection and classification. The transmitted and backscattered signals
were calculated by finding out the solution of the Helmholtz wave equation
in the frequency domain using the FEM. FE mesh convergence analysis for
electric field values and comparison between different types of iterative
solver were also performed to obtain error-free results in minimal
computational time. At the end, specific absorption rate analysis was
conducted to examine the ionization effects of MW signals to a 3D human
head model. Through computer simulations, it is foreseen that MW imaging
may efficiently be exploited to locate and differentiate two types of
brain stroke by detecting abnormal tissues’ dielectric properties. A
significant contrast between electric field values of the normal and
stroke-affected brain tissues was observed at the stroke location. This is
a step towards generating MW scattering information for the development of
an efficient image reconstruction algorithm.