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

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior