Research On The Algorithm And Application Of Pulmonary Electrical Impedance Tomography

Electrical Impedance Tomography(EIT)is an emerging imaging technology applied in the field of medical imaging.It applies a certain frequency of safe excitation current to the human body through the electrodes placed on the surface of the measured area of the human body,and simultaneously measures the voltage signals of the remaining electrodes to obtain the electrophysiological information of the measured tissue or organ,and finally uses a specific image reconstruction algorithm.Image reconstruction is performed on the impedance distribution to obtain a tomographic image of the measured tissue.Compared with traditional medical imaging technologies such as Xray and CT,EIT technology has the advantages of non-invasiveness,non-radiation,low cost,and continuous detection.Provide technical support and have good future application prospects.However,the current EIT imaging algorithm is not very mature,the algorithm is sensitive to noise,and the EIT image lacks quantifiable indicators.Aiming at these problems,the paper compares a variety of imaging algorithms,and proposes a more robust method under noise.In addition,the quantification method of EIT image is studied.The main research contents of the paper are as follows:1)A finite element simulation model of the EIT positive problem is established.Given the initial conductivity of the model,the potential distribution and boundary voltage data in the field can be obtained.The forward simulation model can generate a large amount of simulation data for subsequent image reconstruction and algorithm performance evaluation.2)The EIT image reconstruction algorithm is studied,and an image reconstruction algorithm based on the alternating direction multiplier method to solve the L1 paradigm is proposed.Mathematical analysis and comparison of imaging parameters were carried out with the traditional linear back-projection algorithm,the one-step Gauss-Newton error method and the GREIT method.The results in the reconstruction of the simulated data show that: 1.The target position and shape of the reconstructed image by the GREIT algorithm are accurate,and the outline is clear,but it is easily disturbed by noise,and the comprehensive evaluation is the best in the reconstruction of noise-free data;2.The reconstructed image of the ADMM method The target position is accurate,but the reconstruction result of the target is partially deformed,and the noise interference is minimal in the data reconstruction with added noise,which shows that the method has good anti-interference performance.The clinical data show that both algorithms can effectively reconstruct the pulmonary ventilation,and have the possibility of being applied to the clinical practice of pulmonary function imaging.3)Based on the reconstructed images,a series of quantitative indicators that can measure lung function parameters are extracted,including the region of interest,the optimal compliance of pixels,and the lung inflation-collapse ratio.Image reconstruction and parameter calculation were performed on a group of EIT data collected from patients with respiratory distress.The results show that both reconstruction methods can calculate effective parameter indicators from the reconstruction results of experimental data,and can identify lung hyperventilation and collapse.This information provides a basis for the setting of PEEP value during mechanical ventilation.