SPECIAL: Single-Shot Projection Error Correction Integrated Adversarial Learning for Limited-Angle CT

Limited-angle CT is an indispensable tool for some practical applications when the projection data can be only collected within a limited-angle range due to the constraints of scanning conditions. However, the limited-angle scanning mode will lead to severely degraded images with excessive artifacts. Meanwhile, existing methods fail to reconstruct satisfactory images in limited-angle CT because of the unguaranteed measurement consistency caused by serious projection missing. In this paper, we developed a method termed Single-shot Projection Error Correction Integrated Adversarial Learning (SPECIAL) progressive-improvement strategy, which could effectively combine the complementary information contained in the image domain and projection domain, and greatly improve the reconstructions at the expense of small computational cost. Specifically, enhanced adversarial learning is used in different stages to remove artifacts without losing high-frequency component. A projection error correction module is used to boost the performance in high-attenuation tissue restoration. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method could make a promising improvement on artifact removal, edge preservation and tiny structure restoration.