LAUSR

Spectral computed tomography (CT) can provide narrow-energy-width reconstructed images, thereby suppressing beam hardening artifacts and providing rich attenuation information for component characterization. We propose a statistical iterative spectral CT imaging method based on blind separation of polychromatic projections to improve the accuracy of narrow-energy-width image decomposition. For direct inversion in blind scenarios, we introduce the system matrix into the X-ray multispectral forward model to reduce indirect errors. A constrained optimization problem with edge-preserving regularization is established and decomposed into two sub-problems to be alternately solved. Experiments indicate that the novel algorithm obtains more accurate narrow-energy-width images than the state-of-the-art method.