Study On Multi-frequency Image Reconstruction Algorithms For Electrical Impedance Tomography

As a new medical detection technology,electrical impedance tomography has been a key research technique in the field of clinical medical detection in recent decades.The medical detection technology is not only safe for human body,but also has the advantages of simple device,convenient detection and low equipment cost,so it can be widely applied.Therefore,it is a new medical detection technology worthy of further research.It has been developing rapidly in recent decades,but there are still some difficult problems to overcome,so far,it has not been widely applied.Therefore,it is very important for the development of clinical medical detection technology to study the difficult points of electrical impedance tomography technology.In this paper,the main work of electrical impedance tomography is:First,the mathematical physical model of Multi-frequency Electrical Impedance Tomography(MFEIT)is theoretically derived.Secondly,the weighted method of frequency differential impedance imaging is studied in the MFEIT study.The background of the measured region is normalized by studying the double weighting method.Third,anti-frequency electrical impedance tomography is studied based on the double weighting method.The anti-frequency electrical impedance tomography increase the measurement data,reduce measurement error,so as to improve the imaging quality.Fourth,aiming at the serious ill posed problem in electrical impedance tomography,partial least squares algorithm is applied to solve the inverse problem.Image reconstruction of voltage simulation data and voltage measured data is carried out,and compared with the traditional least squares regularization algorithm.The results show that the reconstruction effect of anti-frequency electrical impedance tomography is better than the dual frequency method.Partial least squares is applied to inverse problem solving,which can improve the noise immunity of multi-frequency electrical impedance tomography and determine the location and basic shape of the imaging target.