  A major challenge in computed tomography is reconstructing objects from
incomplete data. An increasingly popular solution for these problems is to
incorporate deep learning models into reconstruction algorithms. This study
introduces a novel approach by integrating a Fourier neural operator (FNO) into
the Filtered Backprojection (FBP) reconstruction method, yielding the FNO back
projection (FNO-BP) network. We employ moment conditions for sinogram
extrapolation to assist the model in mitigating artefacts from limited data.
Notably, our deep learning architecture maintains a runtime comparable to
classical filtered back projection (FBP) reconstructions, ensuring swift
performance during both inference and training. We assess our reconstruction
method in the context of the Helsinki Tomography Challenge 2022 and also
compare it against regular FBP methods.
