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 scattering from a three dimensional (3-D) anthropomorphic human head model is presented. It is the first time that Finite Element Method (FEM) has been deployed to study the microwave scattering phenomenon of a 3-D realistic head model for brain stroke detection. A major contribution of this paper is to add anatomically more realistic details into the human head model compared to the literature available to-date. Using MRI database, a 3-D 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 to brain tissues were assigned using the same mapping technique. To mimic the simulation setup, an 8-elements antenna array around the head model was designed using dipole antennas. Two types of brain stroke (Hemorrhagic and Ischemic) at various locations inside the head model were then analyzed for possible detection and classification. The transmitted and backscattered signals were calculated by finding out the solution of Helmholtz wave equation in frequency-domain using 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 a minimum computational time. At the end, a Specific Absorption Rate analysis was conducted to examine the ionization effects of microwave signals to a 3-D human head model. Through computer simulations, it is foreseen that microwave imaging may efficiently be exploited to locate and differentiate two types of brain stroke by detecting abnormal tissue’s 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 microwave scattering information for the development of an efficient image reconstruction algorithm