Title: Deep Learning for Computed Tomography Image Reconstruction from Insufficient Data
Abstract: Computed tomography (CT) image reconstruction from insufficient data is a severely ill-posed inverse problem. Conventional methods solely have very limited performance to address this problem. Deep learning has achieved impressive results in solving various inverse problems. 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 talk, we investigate the robustness of deep learning in CT image reconstruction first. 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 conventional methods and deep learning: First, a prior image is generated by deep learning. Afterwards, unmeasured data are inpainted by forward projection of the prior image. Finally, a final image is reconstructed by a conventional method, integrating data consistency for measured data and learned prior information for missing data. The DCR method is demonstrated in two scenarios: image reconstruction from limited-angle data and truncated data.