Research On The Optimization Algorithm Of Hybrid Variation Reconstruction For Lung Electrical Impedance Tomography

Electrical impedance tomography(EIT)is a medical imaging method with three advantages: no damage,no radiation,continuous image monitoring and functional imaging.EIT can provide important information related to physiological or pathological changes of human organs or tissues.Pulmonary electrical impedance imaging is very suitable for real-time monitoring of lung ventilation in intensive care unit,especially for alveolar hyperinflation injury,pulmonary edema or emphysema,which can provide a basis for the development of ventilator ventilation strategy.Helps to detect lung disease as early as possible and gain valuable time for the prevention,diagnosis and treatment of related lung diseases.However,in the prior art,when the bioelectrical impedance imaging technique is applied to perform electrical characteristic imaging,the contrast of the obtained image is still low,therefore,this paper proposes an electrical impedance imaging method based on hybrid variation reconstruction algorithm.This paper is supported by the Tianjin Applied Basic and Frontier Technology Research Program and the Natural Science Foundation of Hebei Province.It has conducted in-depth research on positive problem calculation,inverse problem solving,image reconstruction algorithm and imaging experiment of electrical impedance imaging.The following aspects of work:1.The finite element method was used to model the EIT positive problem.The 2-D and 3-D physical models and the real lung model were established.The positive problem calculation was carried out,and the model boundary voltage distribution was simulated and analyzed.It laid the foundation.for inverse problem solving and image reconstruction2.A hybrid variation reconstruction algorithm is proposed which is based on the Total variation regularization algorithm and the Tikhonov regularization algorithm,and the objective function of the hybrid variation reconstruction algorithm is constructed.Inverse problem solving and image reconstruction were performed by total variation regularization algorithm,Tikhonov regularization algorithm and the hybrid variation reconstruction algorithm respectively,and the image reconstruction results were compared and analyzed.3.The regularized parameters are determined by the optimized L-curve method.This method provides flexibility for solving inverse problems in the inverse problem solving process;the steepest descent method is used to solve the minimum value of the objective function of the hybrid variation reconstruction algorithm.This method only needs to solve the first derivative,which occupies less storage unit and solves faster.4.Using the physical model designed in this paper,the electrical impedance imaging experiment was carried out.The pulmonary ventilation process was monitored by a simple ventilator.A lung imaging experiment was performed using a set of pulmonary electrical impedance imaging prototypes built by our group.The established model and the proposed hybrid variation reconstruction algorithm were verified and proved to be usable and feasible.