Research On Lung Electrical Impedance Imaging Algorithm Based On Structural Information

Electrical impedance tomography(EIT)is a new generation of medical imaging technology that has emerged in the past 30 years.It applies a safe excitation current to electrodes placed on the body surface,and simultaneously measures the voltage changes of the corresponding electrodes.According to the image reconstruction algorithm,the distribution of electrical characteristics inside the human body is reconstructed.This technology can not only obtain anatomical structure information,but also provide rich functional information,and has a wide application prospect in the field of biomedical imaging.However,the problem of EIT image reconstruction is an ill-conditioned nonlinear inverse problem.Overcoming the ill-conditioned nature of EIT images is the key and difficult point of EIT technology,and it is also the focus of this paper.In recent years,sparse and low-rank theoretical methods have been introduced into EIT imaging.From the perspective of local spatial correlation and temporal correlation,the EIT imaging algorithm has been improved to effectively improve the imaging quality.This paper further combines the sparse and low-rank methods to make full use of the temporal-spatial correlation information of dynamic EIT imaging to ensure the imaging speed and further improve the imaging quality.Based on this,a new method is applied to lung imaging to evaluate lung motion parameters.In response to the above problems,this topic focuses on the study of pulmonary electrical impedance imaging based on structural information.The main tasks are as follows:1.A dynamic EIT imaging method based on a combination of low rank and sparseness is proposed.In order to make the algorithm suitable for imaging conductivity distribution of human thorax,which is an irregular-shaped field.In this paper,the spatial-temporal properties have been fully utilized,a mathematical model for EIT reconstruction is built upon a combination of the low-rank and the sparsity theories.In addition to the low-rank method based on the nuclear norm constraint,the patch-based sparse method is also used to obtain the spatial features of a reconstructed image,according to the characteristic of an irregular boundary for the EIT image.The imaging results are compared with the reconstruction results of the traditional algorithms.The experimental results demonstrate better performance of the new method compared with the traditional methods.The effectiveness of the proposed scheme is verified.2.The 3D thorax models of different breathing states are built.The contours of human chest and lung are extracted as priori information based on CT image sequences.3D thorax and lung models under the states of end-inspiratory and end-expiratory are built.EIT lung images of the two states are reconstructed based on different reconstruction algorithms.The distinguishability of EIT images under different breathing states are proved.3.In order to detect pulmonary movement between different breathing states.Lung images were extracted under end-inspiratory and end-expiratory states,and the state of lung movement during breathing was evaluated by calculating the center position of the lungs in different states.The feasibility of EIT for pulmonary breathing monitoring is proved.