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Adaptive backpropagation neural algorithm for limited-angle CT image reconstruction

The proposed system for CT image reconstruction is structured with three layers of neurons. In our previous work, we used the resilient backpropagation(Rprop) instead of the straight BP to modify the network weights. The basic idea is to minimize the error between the projections of the original image and of the reconstructed image. We noticed that the system performance depends on the initial status of the network. Based on this observation, we propose a novel approach for choosing optimal values of the connection weights. The experimental results indicate that the new method can find a satisfactory solution despite that only a few projections are available.