The effects of geometric distortion correction on motion realignment in fMRI

Subject motion is well recognized as a significant impediment to resolution and sensitivity in functional magnetic resonance imaging (fMRI). A parallel confounder to fMRI data quality is geometric image distortion, particularly at high field strengths, due to susceptibility-induced magnetic field inhomogeneity. Consequently, many high-field echo-planar imaging methods incorporate a post-processing distortion correction by acquiring a field map of the sample prior to the fMRI measurement. However, field mapping methods impose a spatial mask on the data, since field information is only obtainable from regions with adequate signal-to-noise ratio (SNR). This masking, when applied to subsequent images in the fMRI time series, can clip the effects of motion, resulting in inaccurate estimation and correction of motion-based changes in the images.

The effects of geometric distortion correction on automated realignment (motion correction) of fMRI data are investigated from data acquired at 4 T. The results of image realignment with and without prior application of distortion correction are compared, using the estimated motion parameters and overall image realignment as metrics.

The application of field-map-based distortion correction prior to image realignment reduces the amount of motion detected by a standard motion correction algorithm. Moreover, motion correction applied before distortion correction is shown to result in superior realignment of motion-correction images.

It is preferable to perform motion realignment prior to correcting for geometric distortion.