  Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly
ill-posed problem. It is often used in applications that require low radiation
dose in clinical CT, rapid industrial scanning, or fixed-gantry CT. Existing
analytic or iterative algorithms generally produce poorly reconstructed images,
severely deteriorated by artifacts and noise, especially when the number of
x-ray projections is considerably low. This paper presents a deep
network-driven approach to address extreme few-view CT by incorporating
convolutional neural network-based inference into state-of-the-art iterative
reconstruction. The proposed method interprets few-view sinogram data using
attention-based deep networks to infer the reconstructed image. The predicted
image is then used as prior knowledge in the iterative algorithm for final
reconstruction. We demonstrate effectiveness of the proposed approach by
performing reconstruction experiments on a chest CT dataset.
