3D Brain Anomaly Tissues Detection Based On Electrical Impedance Tomography Simulation Research

Bioelectrical impedance tomography technique is a new, noninvasive imaging modality, which not only caused great concern in the field of biomedical engineering, but also made more scientists pay attentio n to research of it in the industrial monitoring and non-destructive testing. The electrical properties of tissue are the basic feature of cell life activities. Analyzing the state of physiological structure, specific tissue and cell pathology by the phenomena of biological tissue, contributes to a better understanding to the cell biological phenomena of the formation mechanism, and provides valuable information of diagnosed medical.This work consists of the following four parts: Firstly, a brief introduction of the meaning and context of the theme : the traditional electrical impedance tomography(EIT), magnetic resonance electrical impedance tomography(MREIT) and the development of EIT and MREIT. Secondly, In order to locate the pathological changes of the head impedance effectively, a series of computer simulation were conducted on a finite element model and a realistic-geometry head model, and the differential evolution algorithm was adopted to reconstruct the conductivity image of the head tissue. Then, the MREIT which including forward problem, inverse problem were discussed in detail. A series of computer simulation were conducted on a realistic-geometry head model to prove the magnetic field is more stable than the electric field in the forward problem. As to the inverse problem: the 3D-MREIT algorithm was proposed based on differential evolution algorithm ideas which utilizes single direction of magnetic flux density measured values to reconstruct the impedance of the head tissue, and which not only solves the rotation problem in MREIT effectively, but also could be accurately reconstruct the impedance of the head tissue and successfully detect lesion area presently. Lastly, on the base of the accuracy reconstructed image to improve the insufficient of the 3D MREIT algorithm, the optimized algorithm significantly reduces the reconstruction time. The reconstructed images show higher quality and are reliable and it has a potential value in the future clinical applications.