Prospective motion correction in functional MRI

Abstract

Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no-motion conditions, which motivates further research in the area of adaptive dynamic imaging.

Introduction

Magnetic resonance imaging (MRI) is an indispensable tool for studying brain morphology and function. Although MRI provides great diagnostic and prognostic value, it is also notorious for a host of artefacts, which often affect the quality of the acquired data and reliability of the conclusions. Motion artefacts in traditional MR imaging have an extremely volatile appearance, but typically they can be represented as a combination of blurring and ghosting (Lauzon and Rutt, 1993, Van de Walle et al., 1997, Wood and Henkelman, 1985).The exact manifestation of motion artefacts is related to the way in which inconsistent raw data acquired in different motion states are combined into a single k-space dataset prior to the Fourier transform. These artefacts arise mainly due to the prolonged period of time required to form a complete k-space dataset. For more detail on artefacts in traditional MRI interested reader is referred to recent reviews on the topic (Godenschweger et al., 2016, Zaitsev et al., 2015).In contrast, functional MRI (fMRI) is typically carried out using 2D single-shot signal readouts (Tsao, 2010). Such readout modules are in most cases under 100ms in duration and are hence said to “freeze” motion, with regards to the majority of physiological motion types. Therefore individual 2D images resulting from single-shot acquisitions are indeed free of the “classical” MRI motion artefacts. Nonetheless, in the case of fMRI, volumetric data are required, which are produced by the sequential acquisition of multiple 2D slices, with characteristic acquisition times in the range of one to several seconds. Even though recent approaches such as simultaneous multi-slice (SMS) allow for shortening of these times by a significant factor (Barth et al., 2016), fast physiological motion may still produce volume distortions and cause crosstalk between different slices, resulting in intra-volume data inconsistencies. More importantly, fMRI acquisitions consist of repetitive scanning of the same volume, in which the temporal evolution of the signal in every voxel serves as a basis for the statistical analysis (Cox, 1996, Friston et al., 1995, Goebel et al., 2006). Motion in this case results in inconsistencies between the subsequently acquired image volumes across the time series, giving rise to inter-volume data instabilities. Although these may not lead to apparent artefacts in the images, they may substantially deteriorate the statistical analysis and distort the results.