ECT image reconstruction based on RBF neural networks
Qiang Zhou · Shenyang Gongye Daxue xuebao · 2007
The algorithm most commonly used for ECT image reconstruction is the linear back-projection(LBP),where the non-linear relationship between the permittivity distribution and capacitance measurements is usually approximated to the linear.Because the non-linear mapping ability of neural networks can avoid such linear approximation,the image reconstruction of 16-electrode ECT system based on RBF neural networks was discussed.RBF neural networks were trained to convert the electrode capacitance measurements to the permittivity distributions in image region.The number of hidden nodes in RBF neural networks was determined using maximal matrix element method,and the center and width of RBF function were determined using the nearest neighbor-clustering algorithm.Simulation results indicate that this image reconstruction algorithm can provide images superior to those obtained by the LBP algorithm within a similar reconstruction time.