Sparse-view Phase-Contrast and Attenuation-Based CT Reconstruction Utilizing Model-Driven Deep Learning

Grating-based X-ray phase-contrast imaging enhances the contrast of imaged objects, particularly soft tissues. However, the radiation dose in computed tomography (CT) is generally excessive owing to the complex collection scheme. Sparse-view CT collection reduces the radiation dose, but with reduced resolution and reconstructed artifacts particularly in analytical reconstruction methods. Recently, deep learning has been employed in sparse-view CT reconstruction and achieved state-of-the-art results. Nevertheless, its low generalization performance and requirement for abundant training datasets have hindered the practical application of deep learning in phase-contrast CT. In this study, a CT model was used to generate a substantial number of simulated training datasets, thereby circumventing the need for experimental datasets. By training a network with simulated training datasets, the proposed method achieves high generalization performance in attenuation-based CT and phase-contrast CT, despite the lack of sufficient experimental datasets. In experiments utilizing only half of the CT data, our proposed method obtained an image quality comparable to that of the filtered back-projection algorithm with full-view projection. The proposed method simultaneously addresses two challenges in phase-contrast three-dimensional imaging, namely, the lack of experimental datasets and the high exposure dose, through model-driven deep learning. This method significantly accelerates the practical application of phase-contrast CT.