  Image reconstruction from insufficient data is common in computed tomography
(CT), e.g., image reconstruction from truncated data, limited-angle data and
sparse-view data. Deep learning has achieved impressive results in this field.
However, the robustness of deep learning methods is still a concern for
clinical applications due to the following two challenges: a) With limited
access to sufficient training data, a learned deep learning model may not
generalize well to unseen data; b) Deep learning models are sensitive to noise.
Therefore, the quality of images processed by neural networks only may be
inadequate. In this work, we investigate the robustness of deep learning in CT
image reconstruction by showing false negative and false positive lesion cases.
Since learning-based images with incorrect structures are likely not consistent
with measured projection data, we propose a data consistent reconstruction
(DCR) method to improve their image quality, which combines the advantages of
compressed sensing and deep learning: First, a prior image is generated by deep
learning. Afterwards, unmeasured projection data are inpainted by forward
projection of the prior image. Finally, iterative reconstruction with
reweighted total variation regularization is applied, integrating data
consistency for measured data and learned prior information for missing data.
The efficacy of the proposed method is demonstrated in cone-beam CT with
truncated data, limited-angle data and sparse-view data, respectively. For
example, for truncated data, DCR achieves a mean root-mean-square error of 24
HU and a mean structure similarity index of 0.999 inside the field-of-view for
different patients in the noisy case, while the state-of-the-art U-Net method
achieves 55 HU and 0.995 respectively for these two metrics.
