Characterization and correction of interpolation effects in the realignment of fMRI time series

Subject motion in functional magnetic resonance imaging (fMRI) studies can be accurately estimated using realignment algorithms. However, residual changes in signal intensity arising from motion have been identified in the data even after realignment of the image time series. The nature of these artifacts is characterized using simulated displacements of an fMRI image and is attributed to interpolation errors introduced by the resampling inherent within realignment. A correction scheme that uses a periodic function of the estimated displacements to remove interpolation errors from the image time series on a voxel-by-voxel basis is proposed. The artifacts are investigated using a brain phantom to avoid physiological confounds. Small- and large-scale systematic displacements show that the artifacts have the same form as revealed by the simulated displacements. A randomly displaced phantom and a human subject are used to demonstrate that interpolation errors are minimized using the correction.