Upon visual inspection of intra-subject rigid body registrations in large studies, we have observed higher than desired rate of unsatisfactory alignments. To address misregistartions, we designed a battery of 13 candidate transformations, one of which was selected as best during visual inspection. Tediousness of the inspections stimulated development of artificial observer to aid and subsequently to replace the human inspector. Here, we describe artificial observer MARLINA, characterize its ability to identify the best rigid body transformation as compared to human inspectors and propose it as a future cost function.
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