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It was shown that images reconstructed for transmission tomography with iterative maximum likelihood (ML) algorithms exhibit a higher signal-to-noise ratio than images reconstructed with filtered back-projection type algorithms. However, a drawback of ML reconstruction in particular and iterative reconstruction in general is the requirement that the reconstructed field of view (FOV) has to cover the whole volume that contributes to the absorption. In the case of a high resolution reconstruction, this demands a huge number of voxels. This article shows how an iterative ML reconstruction can be limited to a region of interest (ROI) without losing the advantages of a ML reconstruction. Compared with a full FOV ML reconstruction, the reconstruction speed is mainly increased by reducing the number of voxels which are necessary for a ROI reconstruction. In addition, the speed of convergence is increased.
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Ziegler et al. (2008) studied this question.
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