Limited-Angle Tomography Using a Neural Network as the Objective Function

In limited-angle tomography, the system of imaging equations is underdetermined, and a naïve reconstruction may not have any practical values. Additional information is needed to augment the data so that a useful image can be reconstructed. This additional information is usually implemented as a Bayesian term in the objective function for an iterative optimization procedure. The state-of-the-art augmented information is the total variation (TV) norm of the image. The TV norm enforces a smooth image with sharp edges. The novelty of this paper is a new Bayesian term that is in the form of a neural network. This neural network is a classifier trained by images reconstructed by full and limited-angle projections. The impact of the proposed method is that the information provided by the neural network contains more features of the images than the TV norm and better reconstruction is expected. Computer simulations are carried out and presented.