  Computed tomography (CT) reconstruction from X-ray projections acquired
within a limited angle range is challenging, especially when the angle range is
extremely small. Both analytical and iterative models need more projections for
effective modeling. Deep learning methods have gained prevalence due to their
excellent reconstruction performances, but such success is mainly limited
within the same dataset and does not generalize across datasets with different
distributions. Hereby we propose ExtraPolationNetwork for limited-angle CT
reconstruction via the introduction of a sinogram extrapolation module, which
is theoretically justified. The module complements extra sinogram information
and boots model generalizability. Extensive experimental results show that our
reconstruction model achieves state-of-the-art performance on NIH-AAPM dataset,
similar to existing approaches. More importantly, we show that using such a
sinogram extrapolation module significantly improves the generalization
capability of the model on unseen datasets (e.g., COVID-19 and LIDC datasets)
when compared to existing approaches.
