  Low-dose Computed Tomography is a common issue in reality. Current reduction,
sparse sampling and limited-view scanning can all cause it. Between them,
limited-view CT is general in the industry due to inevitable mechanical and
physical limitation. However, limited-view CT can cause serious imaging problem
on account of its massive information loss. Thus, we should effectively utilize
the scant prior information to perform completion. It is an undeniable fact
that CT imaging slices are extremely dense, which leads to high continuity
between successive images. We realized that fully exploit the spatial
correlation between consecutive frames can significantly improve restoration
results in video inpainting. Inspired by this, we propose a deep learning-based
three-stage algorithm that hoist limited-view CT imaging quality based on
spatial information. In stage one, to better utilize prior information in the
Radon domain, we design an adversarial autoencoder to complement the Radon
data. In the second stage, a model is built to perform inpainting based on
spatial continuity in the image domain. At this point, we have roughly restored
the imaging, while its texture still needs to be finely repaired. Hence, we
propose a model to accurately restore the image in stage three, and finally
achieve an ideal inpainting result. In addition, we adopt FBP instead of
SART-TV to make our algorithm more suitable for real-time use. In the
experiment, we restore and reconstruct the Radon data that has been cut the
rear one-third part, they achieve PSNR of 40.209, SSIM of 0.943, while
precisely present the texture.
