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Objective The algorithm，assessing quality using image registration circuits(AQUIRC)，can estimate registration error within an image network with multiple registration circuits relying solely on deformation fields.In this paper，the performance of AQUIRC in estimating registration errors associated with affine transformation is studied to give a more comprehensive understanding of the algorithm.Methods Linear transformations including rotation，scaling，shear and translation of different deformation degree are designed to simulate registration errors among brain magnetic resonance(MR)images.The linear correlation between the registration errors estimated by AQUIRC and target registration error(TRE)is analyzed at non-discriminatory uniform sampling locations，and the regional distributions of both are shown.Results In rotation，scaling and shear cases，the correlation between AQUIRC’s estimation and TRE is quite high，and their regional distributions are almost the same.In translation cases，however，no correlation is observed.Nevertheless，the correlation can be improved to some extent if translation error is combined with other type of transformation errors whose deformation degree is big enough.Conclusions AQUIRC can accurately estimate registration errors associated with rotation，scaling，and shear transformation，yet cannot correctly estimate errors caused by translation.