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

Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images