  Tomography deals with the reconstruction of objects from their projections,
acquired along a range of angles. Discrete tomography is concerned with objects
that consist of a small number of materials, which makes it possible to compute
accurate reconstructions from highly limited projection data. For cases where
the allowed intensity values in the reconstruction are known a priori, the
discrete algebraic reconstruction technique (DART) has shown to yield accurate
reconstructions from few projections. However, a key limitation is that the
benefit of DART diminishes as the number of different materials increases. Many
tomographic imaging techniques can simultaneously record tomographic data at
multiple channels, each corresponding to a different weighting of the materials
in the object. Whenever projection data from more than one channel is
available, this additional information can potentially be exploited by the
reconstruction algorithm. In this paper we present Multi-Channel DART
(MC-DART), which deals effectively with multi-channel data. This class of
algorithms is a generalization of DART to multiple channels and combines the
information for each separate channel-reconstruction in a multi-channel
segmentation step. We demonstrate that in a range of simulation experiments,
MC-DART is capable of producing more accurate reconstructions compared to
single-channel DART.
