  Spectral computed tomography (CT) has attracted much attention in radiation
dose reduction, metal artifacts removal, tissue quantification and material
discrimination. The x-ray energy spectrum is divided into several bins, each
energy-bin-specific projection has a low signal-noise-ratio (SNR) than the
current-integrating counterpart, which makes image reconstruction a unique
challenge. Traditional wisdom is to use prior knowledge based iterative
methods. However, this kind of methods demands a great computational cost.
Inspired by deep learning, here we first develop a deep learning based
reconstruction method; i.e., U-net with L_p^p-norm, Total variation, Residual
learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the
Various Multi-scale Feature Fusion and Multichannel Filtering Enhancement with
a denser connection encoding architecture for residual learning and feature
fusion. To address the image deblurring problem associated with the
$L_2^2$-loss, we propose a general $L_p^p$-loss, $p>0$ Furthermore, the images
from different energy bins share similar structures of the same object, the
regularization characterizing correlations of different energy bins is
incorporated into the $L_p^p$-loss function, which helps unify the deep
learning based methods with traditional compressed sensing based methods.
Finally, the anisotropically weighted total variation is employed to
characterize the sparsity in the spatial-spectral domain to regularize the
proposed network. In particular, we validate our ULTRA networks on three
large-scale spectral CT datasets, and obtain excellent results relative to the
competing algorithms. In conclusion, our quantitative and qualitative results
in numerical simulation and preclinical experiments demonstrate that our
proposed approach is accurate, efficient and robust for high-quality spectral
CT image reconstruction.
