Reconstruction of CT images by the backpropagation algorithm

A new and modified neural network model is proposed for CT image reconstruction from four projection data only. The model uses the well known backpropagation delta rule for adaptation of its weights. In addition to the error in projection data of the image being reconstructed, the proposed network makes use of errors in pixels between a filtered image and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.