  Spectral computed tomography (CT) is an emerging technology capable of
providing high chemical specificity, which is crucial for many applications
such as detecting threats in luggage. This type of application requires both
fast and high-quality image reconstruction and is often based on sparse-view
(few) projections. The conventional filtered back projection (FBP) method is
fast but it produces low-quality images dominated by noise and artifacts in
sparse-view CT. Iterative methods with, e.g., total variation regularizers can
circumvent that but they are computationally expensive, as the computational
load proportionally increases with the number of spectral channels. Instead, we
propose an approach for fast reconstruction of sparse-view spectral CT data
using a U-Net convolutional neural network architecture with multi-channel
input and output. The network is trained to output high-quality CT images from
FBP input image reconstructions. Our method is fast at run-time and because the
internal convolutions are shared between the channels, the computational load
increases only at the first and last layers, making it an efficient approach to
process spectral data with a large number of channels. We have validated our
approach using real CT scans. Our results show qualitatively and quantitatively
that our approach outperforms the state-of-the-art iterative methods.
Furthermore, the results indicate that the network can exploit the coupling of
the channels to enhance the overall quality and robustness.
