Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data

Abstract

Longitudinal and multi-site clinical studies create the imperative to characterize and correct technological sources of variance that limit image reproducibility in high-resolution structural MRI studies, thus facilitating precise, quantitative, platform-independent, multi-site evaluation. In this work, we investigated the effects that imaging gradient non-linearity have on reproducibility of multi-site human MRI. We applied an image distortion correction method based on spherical harmonics description of the gradients and verified the accuracy of the method using phantom data. The correction method was then applied to the brain image data from a group of subjects scanned twice at multiple sites having different 1.5 T platforms. Within-site and across-site variability of the image data was assessed by evaluating voxel-based image intensity reproducibility. The image intensity reproducibility of the human brain data was significantly improved with distortion correction, suggesting that this method may offer improved reproducibility in morphometry studies. We provide the source code for the gradient distortion algorithm together with the phantom data.

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

“…FS processing has been demonstrated to have high test–retest reliability in identifying and measuring various aspects of brain anatomy across scanner manufacturers and field strengths [Dickerson et al, 2008; Han et al, 2006; Jovicich et al, 2006; Morey et al, 2010; Reuter et al, 2012]. However, it is important to point out that the FS processing pipeline requires careful inspection of processed outputs to ensure that segmentations and reconstructions are spatially accurate and anatomically correct.…”

“…Consequently, the accuracy of measures extracted from structural MRI is largely contingent on initial image quality, which is sensitive to multiple sources of variability. For example, differences at the participant level [e.g., gray and white matter intensity contrast; Westlye et al, 2009], and instrument‐related noise [e.g., image gradient distortions; Jovicich et al, 2006] may both significantly influence estimates of brain structure [also see Gronenschild et al, 2012; Han et al, 2006]. …”

Independent spinal cord atrophy measures correlate to motor and sensory deficits in individuals with spinal cord injury

“…13 To remove those image warping effects, correction was performed off-line using inhouse software implemented in Matlab (The MathWorks, Inc., Natick, MA, USA) as described earlier. 14 The gradient field map for the gradient system of the MR scanner was used as provided by the vendor.…”

Hippocampal volume changes in healthy subjects at risk of unipolar depression

“…Both T1 and T2 images were corrected for spatial distortions due to non-linearity in the gradient system of the scanner (Jovicich et al, 2006) . Images generated at each processing stage were visually checked to ensure the quality of the image processing.…”