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The Study on Medical Electrical Impedance Tomography

Author: PengYuan
Tutor: WangShuoZhongï¼›MoYuLong
School: Shanghai University
Course: Communication and Information System
Keywords: Electrical impedance tomography image reconstruction ill-conditioned finite element method adaptive refinement regularization neural network
CLC: R318.0
Type: PhD thesis
Year: 2004
Downloads: 339
Quote: 3
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Abstract


Electrical impedance tomography is a novelty imaging technique. It reconstructs the internal impedance distribution by the electrical parameter measurements made on the electrodes placed on the surface of the organism. Since the organism has different impedance distribution under the different condition, the reconstructed image can reflect the state of the organism and provides useful information for medical diagnosis. The advantages such as the functional imaging, non-invasive modality and the relative low cost make EIT become a research hot in medical imaging.However, the image reconstruction in EIT is a high ill-conditioned, non-linear, inverse problem. It is the focus of our research. The dissertation has three parts. Firstly, the mathematical and physical models of the image reconstruction in EIT are discussed, and the ill-conditioned is researched deeply. Secondly, several methods used to mitigate the ill-conditioned are proposed which emphasize on the finite element division and algorithm improvement. At last, a 32-electrodes EIT imaging system is developed, and its performance is validated by the results of a series of experiments.The achievements of the dissertation are enumerated below. 1. The effective improvement of finite element division method EIT forward problem is the foundation of imaging reconstruction and the finite element division is usually used for solving it. Traditionally, in the static imaging, the finite element division model will not change during the process of imaging. But generally the initial finite element division is not fit for the problem. So the error introduced by finite element division model will be retained and propagated in the process of imaging, and results bad image. In the dissertation, an adaptive refinement for finite element division based on wavelet analysis is presented, and used in the process of static imaging. The method uses coarse meshes to divide the whole researched region at first, and refines the local meshes during the course of iteration. The adaptive refinement method reduces the scale of finite mesh, and alleviates the degree of the ill-conditioned, and speeds up the process of imaging. It gives a new

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