  Limited angle problem is a challenging issue in x-ray computed tomography
(CT) field. Iterative reconstruction methods that utilize the additional prior
can suppress artifacts and improve image quality, but unfortunately require
increased computation time. An interesting way is to restrain the artifacts in
the images reconstructed from the practical filtered back projection (FBP)
method. Frikel and Quinto have proved that the streak artifacts in FBP results
could be characterized. It indicates that the artifacts created by FBP method
have specific and similar characteristics in a stationary limited-angle
scanning configuration. Based on this understanding, this work aims at
developing a method to extract and suppress specific artifacts of FBP
reconstructions for limited-angle tomography. A data-driven learning-based
method is proposed based on a deep convolutional neural network. An end-to-end
mapping between the FBP and artifact-free images is learned and the implicit
features involving artifacts will be extracted and suppressed via nonlinear
mapping. The qualitative and quantitative evaluations of experimental results
indicate that the proposed method show a stable and prospective performance on
artifacts reduction and detail recovery for limited angle tomography. The
presented strategy provides a simple and efficient approach for improving image
quality of the reconstruction results from limited projection data.
