Image reconstruction from fully-truncated and sparsely-sampled line integrals using iCT-Net

Image reconstruction from line integrals is one of foundations in computed tomography (CT) for medical diagnosis and non-destructive detection purpose. To accurately recover the density function from measurements taken over straight lines, analytic-formula-based or optimization-based inversions have been discovered over the past several decades. Accurate image reconstruction can be achieved if the acquired dataset satisfies data sufficiency conditions and data consistency conditions. However, if these conditions are violated, accurate image reconstruction remains an intellectual challenge provided that significant a priori information about image object and/or physical process of data acquisition need to be incorporated. In this work, we show that a deep learning method based upon a brand new network architecture, termed intelligent CT neural network (iCT-Net), can be employed to discover accurate image reconstruction solutions from fully-truncated and sparsely-sampled line integrals without explicit incorporations of a priori information of either image object or data acquisition process. After a two-stage training, the trained iCT-Net was directly applied to real human subject data to demonstrate the generalizability of iCT-Net to experimental data.