  Objective. Dual-energy computed tomography (DECT) has the potential to
improve contrast, reduce artifacts and the ability to perform material
decomposition in advanced imaging applications. The increased number or
measurements results with a higher radiation dose and it is therefore essential
to reduce either number of projections per energy or the source X-ray
intensity, but this makes tomographic reconstruction more ill-posed.
  Approach. We developed the multi-channel convolutional analysis operator
learning (MCAOL) method to exploit common spatial features within attenuation
images at different energies and we propose an optimization method which
jointly reconstructs the attenuation images at low and high energies with a
mixed norm regularization on the sparse features obtained by pre-trained
convolutional filters through the convolutional analysis operator learning
(CAOL) algorithm.
  Main results. Extensive experiments with simulated and real computed
tomography (CT) data were performed to validate the effectiveness of the
proposed methods and we reported increased reconstruction accuracy compared to
CAOL and iterative methods with single and joint total-variation (TV)
regularization.
  Significance. Qualitative and quantitative results on sparse-views and
low-dose DECT demonstrate that the proposed MCAOL method outperforms both CAOL
applied on each energy independently and several existing state-of-the-art
model-based iterative reconstruction (MBIR) techniques, thus paving the way for
dose reduction.
