Synthesize monochromatic images in spectral CT by dual-domain deep learning

Spectral computed tomography (CT) with photon counting detectors (PCDs) can collect photons by setting different energy bins. It is well acknowledged that PCD-based spectral CT has great potential for lowering radiation dose and improve material discrimination. One critical processing in spectral CT is energy spectrum modelling or spectral information decomposition. In this work, we proposed a dual-domain deep learning (DDDL) method to calibrate a spectral CT system by a neural network. Without explicit energy spectrum and detector response model, we train a neural network to implicitly define the non-linear relationship in spectral CT. Virtual monochromatic attenuation maps are synthesized directly from polychromatic projections. Simulation and real experimental results verified the feasibilities and accuracies of the proposed method.