Skull and cerebrospinal fluid effects on microwave radiation propagation in human brain

Abstract

The determination of microwave absorption distribution in the human brain is necessary for the detection of brain tumors using thermo-acoustic imaging and for removing them using hyperthermia treatment. In contrast to ionizing radiation, hyperthermia treatment can be applied to remove tumors inside the brain without the concern of including secondary malignancies, which typically form from the neuronal cells of the septum pellucidum. The aim of this study is to determine the microwave absorption distribution in an adult human brain and to study the effects of skull and cerebrospinal fluid on the propagation of microwave radiation inside the brain. To this end, we simulate the microwave absorption distribution in a realistic adult brain model (Colin 27) using the mesh-based Monte Carlo (MMC) method. This is because in spite of there being other numerical methods, the MMC does not require a large memory, even for complicated geometries, and its algorithm is simple and easy to implement with low computational cost. The brain model is constructed using high-resolution (1 mm isotropic voxel) and low noise magnetic resonance imaging (MRI) scans and its volume contains voxels, covering the brain completely. Using the MMC method, the radiative transport equation is solved and the absorbed microwave energy distribution in different brain regions is obtained without any fracture or anomaly. The simulation results show that the skull and cerebrospinal fluid guide the microwave radiation and suppress its penetration through deep brain compartments as a shielding factor. These results reveal that the MMC can be used to predict the amount of required energy to increase the temperature inside the tumour during hyperthermia treatment. Our results also show why a deep tumour inside an adult human brain cannot be efficiently treated using hyperthermia treatment. Finally, the accuracy of the presented numerical method is verified using the signal flow graph technique.