A local and iterative neural reconstruction algorithm for cone-beam data

This work presents a new neural algorithm designed for the reconstruction of tomographic images from Cone Beam data. The neural network does not need a training set but uses the line integral of a single x-ray as ground-truth. The algorithm is iterative and based on a set of neural networks that are working locally and sequentially. The proposed strategy was compared with the iterative ART algorithm and the well known filtered backprojection (FBP) method. The results show how the proposed algorithm is much more accurate even in the presence of noise and under conditions of lack of data.