Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking

Nick Todd,Oliver Josephs,Martina F Callaghan,Antoine Lutti,Nikolaus Weiskopf

doi:10.1016/j.neuroimage.2015.03.013

Abstract

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Highlights

Subject motion in functional magnetic resonance imaging is the most important cause of temporal instability in the data
The distribution of tSNR values were very similar for the no motion cases, indicating that the prospective motion correction (PMC) system is not introducing any errors through spurious modifications of the RF pulses and imaging gradients
For all conditions tested at different spatial resolutions and with differing amounts of deliberate head movement, the PMC system was able to improve the time series tSNR by 30% to 40% compared to uncorrected data

Summary

Introduction

Subject motion in functional magnetic resonance imaging (fMRI) is the most important cause of temporal instability in the data. FMRI studies attempt to detect changes from the mean MR signal on the order of 1% and even minor degradation of the data due to motion can mask effects of interest. When motion occurs during an fMRI time series, it is most commonly handled with post-processing methods that assume rigid body motion and attempt to co-register and align all image volumes within the time series (Ashburner and Friston, 2003). These methods are effective for correcting slow intervolume motion, but cannot handle the problem of faster intra-volume motion.

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