as well be assigned to the green or yellow material during segmentation. With two energy dimensions the points are easily separable, since each voxel value lies close to its attenuation cloud center. Note that these spectra are artificial and not likely to occur in real-world examples.

In this paper we present a new class of algorithms that combines DART with multi-channel imaging for solving discrete multi-channel reconstruction problems. Our algorithm can combine the information from multiple channels to produce a final segmentation that is superior to that of the (single-channel) DART algorithm. Note that since this new method is designed by means of modules or subroutines that are interchangeable (as with DART), the method is essentially a class of algorithms providing a framework for dealing with multi-channel data. For simplicity, however, we will frequently call this framework an algorithm.

This paper is structured as follows. Section 2 introduces the multi-channel discrete tomography problem. In Section 3 the DART algorithm is restated and the Multi-Channel DART (MC-DART) algorithm is introduced. Results of experiments with this algorithm are reported in Section 4. Finally, Section 5 presents the conclusions of this study.

\[0.29\][ ![Elementary example of separation difficulties during segmentation. (a) Distribution of 