Magnetic Induction Tomography for Brain Tissue Imaging Based on Conductivity Distribution for Parkinson’s Disease Diagnosis

Abstract

Parkinson's disease is a prevalent neurodegenerative complication defined by the accumulation of alpha synuclein lewy bodies in the brain. Misdiagnosis results widespread of Parkinson’s disease because clinical diagnosis is challenging, underlining a need of a better detection technique, such as non-invasive magnetic induction tomography (MIT) technique. Non-invasive techniques for biological tissues imaging are becoming popular in biomedical engineering field. Therefore, MIT technology as a non-invasive technique has been encouraged in a medical field due to its advancement of technology in diagnosing diseases. The measurement parameters in MIT are passive electromagnetic properties (conductivity, permittivity, permeability) for biological tissue and the most dominant parameter in MIT is conductivity properties. It is uses a phase shift between a primary magnetic field and an induced field caused by a target object's conductivity. As a function of conductivity, the phase shift between the applied and secondary fields is expressed. Thus, the phase shift can be used to characterize the conductivity of a target object. The phase shift between the excitation and induced magnetic fields (EMF and IMF) reflects the change in conductivity in biological tissues. This paper focuses on the virtual simulation by using COMSOL Multi-physics for the design and development of MIT system that emphasizes on single channel magnetic induction tomography for biological tissue (bran tissue) imaging based on conductivity distribution for Parkinson’s disease diagnosis. The develop system employs the use of excitation coils to induce an electromagnetic field (e.m.f) in the brain tissue, which is then measured at the receiving side by sensors. The proposed system is capable of indicating Parkinson’s disease based on conductivity distribution. This method provides the valuable information of the brain abnormality based on differences of conductivities of normal brain and Parkinson’s disease brain tissues.