abstract: Computed Tomography makes use of computer-processed combinations of many X-ray measurements of an object, taken from different angles, and attempts to recover the inner structure of the object from the data. In the case of limited-angle tomography, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, e.g. filtered backprojection (FBP), do not perform well. In this work, we investigate a brand-new method for limited-angle tomography reconstruction, based on the unrolled version of the ISTA algorithm where each iteration contains a convolutional neural network (CNN). The idea of this project has emerged from the observation that the backprojection operator can be approximated by a sequence of convolutions applied to the original object in the wavelet domain. Thus, each CNN has the same structure as the previously mentioned sequence of convolutions.