Grey-value-based 3D registration of functional MRI time-series: comparison of interpolation order and similarity measure

The analysis of functional MR images of the brain such as FMRI and neuro perfusion is significantly limited by movement of the head during image acquisition. Already small motions introduce artifacts in voxel-based statistical analysis and restrict the assessment of functional information. The retrospective compensation of head motion is usually addressed by image registration techniques which spatially align the images of the time-series. In this paper we investigate the relevance of intermediate interpolation during the registration process, similarity measure and optimization scheme by means of statistical consistency of the registration results. Experiments show that cubic and quartic interpolation remarkably improve the consistency when compared to linear methods. The use of larger interpolation kernels, however, does not result in further improvements. Measures based on the mean squared error are successfully applied to FMRI time- series which provide constant tissue-to-image transfer. However, they are not suitable for neuro perfusion imaging since the change of image intensity during the inflow of the contrast agent affords measures typically applied in multi- modality registration. Our results indicate that a recently proposed measure based on local correlation is preferable to mutual information in the case of neuro perfusion.