Abstract
Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B0 field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB0 field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.
Highlights
The quality of magnetic resonance imaging (MRI) data depends on numerous factors, one of the most critical being the homogeneity of the static magnetic field B0 (Jezzard, 2012)
In order to quantitatively assess the impact of the distortions and their correction, we first characterized the geometric distortions and the correction approaches based on the estimated B0 field offset and voxel shift maps (VSMs), and conducted three types of analyses on the uncorrected and distortion-corrected functional magnetic resonance imaging (fMRI) data: (1) estimation of the cost function from the registration between the functional and the structural images; (2) identification and characterization of group resting-state networks (RSNs) [because RSNs have been shown to be present in task-based studies (Di et al, 2013; Cole et al, 2016)]; and (3) mapping of the brain areas related to motion perception in general, and in particular those involved in a visual biological motion (BM) perception task (Chang et al, 2018)
We have characterized the geometric distortions and the correction approaches based on the estimated B0 field offset and voxel shift maps, and quantitatively assessed the impact of geometric distortions on several fMRI data analyses
Summary
The quality of magnetic resonance imaging (MRI) data depends on numerous factors, one of the most critical being the homogeneity of the static magnetic field B0 (Jezzard, 2012). Single-shot gradientecho echo-planar imaging (GE-EPI) sequences are the most prone to geometric distortions, mainly due to the long time interval between the acquisition of adjacent k-space points in the phase-encoding direction, which permits a significant local phase accumulation relative to that produced by the phase-encoding gradients (Jezzard, 2012). Given their ability to acquire wholebrain volumes in a few seconds (or even faster), functional magnetic resonance imaging (fMRI) data has been collected using GE-EPI sequences for decades, and geometric distortions are inevitably present
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