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.

本文提出了一种基于深度卷积神经网络的数据驱动学习方法，用于在限角摄影术下提取和抑制 FBP 重建的特定伪影。 实验结果的定性和定量评估表明，该方法在伪影抑制和细节恢复方面表现出稳定和有前途的性能，为从有限投影数据中重构出的图像质量的提高提供了简单和高效的途径。