Low-rank flat-field correction for artifact reduction in spectral computed tomography

Abstract: Spectral computed tomography has received considerable interest in recent years since spectral measurements contain much richer information about the object of interest. In spectral computed tomography, we are interested in the energy channel-wise reconstructions of the object. However, such reconstructions suffer from low signal-to-noise ratio and share the challenges of conventional low-dose computed tomography such as ring artifacts. Ring artifacts arise from errors in the flat-field correction and can significantly degrade the quality of the reconstruction. We propose an extended flat-field model that exploits high correlation in the spectral flat-fields to reduce ring artifacts in the channel-wise reconstructions. The extended model relies on the assumption that the spectral flat-fields can be well-approximated by a low-rank matrix. Our proposed model works directly on the spectral flat-fields and can be combined with any existing reconstruction model, e.g., filtered back projection and iterative methods. The proposed model is validated on a neutron data set. The results show that our method successfully diminishes ring artifacts and improves the quality of the reconstructions. Moreover, the results indicate that our method is robust; it only needs a single spectral flat-field image, whereas existing methods need multiple spectral flat-field images to reach a similar level of ring reduction.