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

• Published in: NeuroImage (Orlando, Fla.) Vol. 30; no. 2; pp. 436 - 443
• Main Authors: Jovicich, Jorge, Czanner, Silvester, Greve, Douglas, Haley, Elizabeth, van der Kouwe, Andre, Gollub, Randy, Kennedy, David, Schmitt, Franz, Brown, Gregory, MacFall, James, Fischl, Bruce, Dale, Anders
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.04.2006; Elsevier Limited
• Subjects: Algorithms; Gradient non-linearity distortions; Human structural MRI; Methods; Multi-site calibration; Scanners; Studies; Multi-site calibration; Human structural MRI; Gradient non-linearity distortions
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2005.09.046

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.