  Spectral computed tomography (CT) is an emerging technology, that generates a
multienergy attenuation map for the interior of an object and extends the
traditional image volume into a 4D form. Compared with traditional CT based on
energy-integrating detectors, spectral CT can make full use of spectral
information, resulting in high resolution and providing accurate material
quantification. Numerous model-based iterative reconstruction methods have been
proposed for spectral CT reconstruction. However, these methods usually suffer
from difficulties such as laborious parameter selection and expensive
computational costs. In addition, due to the image similarity of different
energy bins, spectral CT usually implies a strong low-rank prior, which has
been widely adopted in current iterative reconstruction models. Singular value
thresholding (SVT) is an effective algorithm to solve the low-rank constrained
model. However, the SVT method requires manual selection of thresholds, which
may lead to suboptimal results. To relieve these problems, in this paper, we
propose a Sparse and lOw-rank UnroLling Network for spectral CT image
reconstruction (SOUL-Net), that learns the parameters and thresholds in a
data-driven manner. Furthermore, a Taylor expansion-based neural network
backpropagation method is introduced to improve the numerical stability. The
qualitative and quantitative results demonstrate that the proposed method
outperforms several representative state-of-the-art algorithms in terms of
detail preservation and artifact reduction.
