Implementation And Research On Generalized Back Projection Algorithm In 3D Eit

Electrical Impedance Tomography (EIT), as a kind of electrical tomography techniques, is one of the significant research projects with the features of non-invasive and functional imaging. EIT attracts much concern from many areas, especially on medical diagnosis and clinical monitoring, since it has many advantages in response speed, simple structure and cost. However, this inverse problem on image reconstruction processes an underdetermined, non-linear and ill-posed nature. The reconstructed images are far from clinical requirements in the aspects of accuracy, stability and resolution. In this dissertation a novel approach of EIT reconstruction algorithm named generalized back projection algorithm is proposed with the purpose of reducing underdetermined conduction, which establishes imaginary node or imaginary element to extend computation range. The algorithm is verified effectiveness by numerical and real experiments.This work was supported in part by the National Natural Science Foundation of China under Grant No. 50937005 and No. 51077040. The main works and results are following.1. Generalized back projection algorithm is proposed based on node back projection algorithm. The information of arbitrary node can be calculated by establishing imaginary node, so that the reconstructed images contain more information. The results of numerical experiments and real experiments verify the applicability of the method for improvement of reconstructed images effect compared with conventional back projection algorithm.2. Based on linearised sensitivity matrix, generalize back projection algorithm is proposed. The imaginary elements are established in the region, so that the information of arbitrary part can be computed to reconstruct more detailed conductivity distribution information in the region. For the reconstructed images between adjacent electrode sections are less effective, further improved generalize back projection algorithm is proposed, which establishes a mapping of solving projection positions of the inner nodes by boundary voltages through imaginary planes. Since the accuracy of projection information is improved, the reconstructed images get better quality. The method is 3-dimentions reconstruction algorithm rather than 2-dimentions tomography algorithm, and is verified by numerical and real experiments.3. A mixed method is presented that generalized back projection algorithm and Newton-type iterative algorithm is combined. The initial value of Newton-type iterative algorithm is estimated from generalized back projection algorithm, so that the initial distribution approximate to real condition. The initial selection strategy prevents empirical selection, and reduces initial error and iterative error at every step, so that computation time is saved.4. To evaluate EIT reconstructed images, four quantitative metrics is presented: position error, resolution, shape deformation and information entropy. The four metrics conduct to evaluate reconstruction algorithms objectively. Compared with conventional back projection algorithm and node back projection algorithm, the images reconstructed by generalized back projection algorithm are more accurate in terms of the four metrics, and the results of improved generalized back projection algorithm are further raised. Compared with conventional Newton-type iterative algorithm, the combination method provides reconstructed images with better effect at the same iterative step.