  Robustness of deep learning methods for limited angle tomography is
challenged by two major factors: a) due to insufficient training data the
network may not generalize well to unseen data; b) deep learning methods are
sensitive to noise. Thus, generating reconstructed images directly from a
neural network appears inadequate. We propose to constrain the reconstructed
images to be consistent with the measured projection data, while the unmeasured
information is complemented by learning based methods. For this purpose, a data
consistent artifact reduction (DCAR) method is introduced: First, a prior image
is generated from an initial limited angle reconstruction via deep learning as
a substitute for missing information. Afterwards, a conventional iterative
reconstruction algorithm is applied, integrating the data consistency in the
measured angular range and the prior information in the missing angular range.
This ensures data integrity in the measured area, while inaccuracies
incorporated by the deep learning prior lie only in areas where no information
is acquired. The proposed DCAR method achieves significant image quality
improvement: for 120-degree cone-beam limited angle tomography more than 10%
RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy
case compared with a state-of-the-art U-Net based method.
