Study On Image Reconstruction Algorithm For Finite Angle Projection Of Magnetic Induction Tomography

Magnetic induction tomography(MIT)is a kind of tomography technology that can image the passive electromagnetic properties of an object,in which the electrical conductivity distribution of the object to be measured is the main imaging target.MIT is concerned with measuring the electromagnetic field distribution of an imaging subject,now therefore has the potential advantage of being non-invasive and non-invasive.The MIT implementation can be achieved by establishing the eddy current model in the forward problem,computing the phase difference data,and then identifying the conductivity distribution in the inverse problem through the reconstruction algorithm.However,the inverse problem of MIT is seriously pathological,and the excitation signal and detection signal of MIT are generated and measured through coils.Therefore,limited by the coil technology and the number of coils,only a limited number of projected data can be collected.In addition,in the actual acquisition process,there are also a series of problems of scanning Angle incongruity and acquisition Angle limitation.In order to solve the problem of limited projection data and low resolution of image in MIT practical application,a filtering back-projection reconstruction algorithm based on total variation constraint was designed.In this paper,the brain model was established according to the anatomical structure of the brain,and the electromagnetic characteristics of each tissue were analyzed.The parameters of the model were set according to the analysis results,and a three-dimensional and three-layer simplified brain model was simulated.The simulation system of brain magnetic induction tomography(BMIT)was established based on the brain model,and the phase data at the detection coil was obtained through the finite element calculation of positive problem,and multiple sets of measured data was used for BMIT imaging.Secondly,by analyzing the projected data and comparing various filtering methods,the data filtering method suitable for MIT is selected.The image reconstruction results show the improvement of image reconstruction accuracy of MIT,which lays a foundation for the further research of reconstruction algorithm.Then,based on the finite angle theory,a total variational filter inverse projection reconstruction algorithm for MIT projection data is proposed.Aiming at the problem of MIT Angle limitation,the projection data obtained by the MIT fan beam scanning method were reconstructed.By using the proposed total variational filtering back-projection reconstruction algorithm,the image reconstruction comparison of the two projection methods of the fan beam scanning and the full cycle scanning was conducted with the same amount of data collected and the same data interval.The reconstruction results are compared with those obtained by common algorithms.The results show that the proposed algorithm improves the resolution of the reconstructed image and the imaging speed is fast.Finally,based on the sparse Angle projection data,the reconstruction of the projection data with different sparsity generated by the fan-beam scanning method with the total variation filtering back-projection method was studied.The data were extracted through the simulation experiment of the establishment of different blood loss models,and compared with the back-projection algorithm and the filtering back-projection algorithm.The validity of the proposed algorithm is confirmed by image reconstruction and result analysis,which lays a foundation for clinical application.