are chosen, as well as the extent of parallelization. If we use SIRT and the multi-channel segmentation method as described above and use a completely sequential implementation, the time complexity of MC-DART is $O(CKn(l+m))$. Because of the dependencies on the methods, we rather speak of a *relative complexity* of MC-DART to DART, which we define as the ratio of the sequential MC-DART complexity to that of DART, irrespective of the subroutines used. This relative time complexity is $O(C)$. The space complexity of this algorithm instance of MC-DART is $O(Cn)$, resulting in a relative space complexity of $O(C)$ as well.

Experimental Results {#sec:Experiments}
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In this section the performance of the described MC-DART framework in terms of reconstruction and segmentation is presented. A series of experiments have been designed in which the number of channels $C$ and different materials $m$ are varied. For each experiment, multiple random phantoms are created. The size of these two-dimensional phantoms is $128 \times 128$ pixels, and each consists of a circular disk containing a random parcellation among $m$ materials in such a way that the total surface is approximately equal for each material. An example of this random phantom is given in Figure \[fig:1a\], where 