  Reconstruction of CT images from a limited set of projections through an
object is important in several applications ranging from medical imaging to
industrial settings. As the number of available projections decreases,
traditional reconstruction techniques such as the FDK algorithm and model-based
iterative reconstruction methods perform poorly. Recently, data-driven methods
such as deep learning-based reconstruction have garnered a lot of attention in
applications because they yield better performance when enough training data is
available. However, even these methods have their limitations when there is a
scarcity of available training data. This work focuses on image reconstruction
in such settings, i.e., when both the number of available CT projections and
the training data is extremely limited. We adopt a sequential reconstruction
approach over several stages using an adversarially trained shallow network for
'destreaking' followed by a data-consistency update in each stage. To deal with
the challenge of limited data, we use image subvolumes to train our method, and
patch aggregation during testing. To deal with the computational challenge of
learning on 3D datasets for 3D reconstruction, we use a hybrid 3D-to-2D mapping
network for the 'destreaking' part. Comparisons to other methods over several
test examples indicate that the proposed method has much potential, when both
the number of projections and available training data are highly limited.
