Research On Methods To Improve Image Quality Of Electrical Impedance Reconstruction

Electrical impedance tomography(EIT),as a new medical imaging technology developed in the past 30 years,can reconstruct the impedance distribution of the interested region.It applies a safe excitation current to the electrodes placed on the body surface and measures the voltage signals simultaneously,then the distribution of the internal electrical characteristics is reproduced according to the image reconstruction algorithm,EIT can obtain anatomical structure information and abundant functional information.EIT becomes a medical research hotspot quickly due to non-radiation,non-invasive,repeatable,reusable and fast functional imaging features,low cost and easy installation.Besides,EIT has a wide range of application prospects.In this paper,EIT's mathematical model of forward problem and the solving of inverse problem are researched based on the development prospects,imaging characteristics,physical models and the research difficulties of electrical impedance tomography.In order to improve the image quality of electrical impedance reconstruction,and the main work includes three aspects:(1)Feature extraction by using principal component analysis algorithm(PCA).The principal component analysis algorithm is used to calculate the respiratory data and extract the characteristic signals in the respiration process.The experimental results are as follows:PCA algorithm can remove the respiratory elements in the original data and extract the heart components,then obtain the cardiac signals in the breath holding process.In the normal breathing process,PCA algorithm can get the conductivity change information,which is the respiratory cycles of the lung.(2)The construction and reconstruction of the lesion models.According to the existing or possible pulmonary diseases,prior information of lung structure and electrical conductivity distribution,the simulation imaging results including healthy lung model and lesion models were solved.The relative error,current density and ventilation index were used to analyze the imaging results,and evaluate the reconstructed image quality objectively.(3)Dual model image reconstruction.Due to the large degree of freedom in solving the EIT inverse problem,a dual-model framework was proposed to minimize the degree of freedom.The fine(high density)FEM is used to solve the forward problems to obtain accurate numerical solutions,and the coarse(lower density)FEM is used to solve the inverse problems,which reduces the ill-posedness of EIT.In addition,the dual-model framework is used to reconstructed 2.5D images,3D images,and rectangular grid images.