and possibly parameters in the smoothing operation. The quality of the reconstructions also depends on the tomographic setup, such as the number of projections and the number of projection angles, and on the complexity of the object, including the number of materials and different attenuation values. Despite the DART algorithm performing well in practice, it is a heuristic method for which no solution guarantees exist [@Bounds]. The DART algorithm is also highly modular. Approaches for segmentation, boundary detection, reconstruction (ARM) and possible smoothing can easily be changed without sacrificing the overall structure of the algorithm. For the multi-channel algorithm proposed in this paper the segmentation phase is adapted to using all multi-channel reconstructions as input.

The complexity of the framework depends on the algorithms that are used for reconstruction and segmentation. In this paper we use SIRT as the reconstruction algorithm and the thresholding segmentation as described above. Therefore, in this case, the DART algorithm has a time complexity of $O(Kn(m+l))$. The space complexity of our implementation is $O(ln)$.

Multi-channel DART {#sec:MCDART}
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We now present the Multi-Channel DART (MC-DART) algorithm and outline its separate building blocks. Most focus will be on the multi-channel segmentation. Note that labeling single-channel images 