Kanakaraj, Praitayini; Yao, Tianyuan; Li, Zhiyuan; Newlin, Nancy R.; Kim, Michael E.; Gao, Chenyu; Yu, Tian; Krishnan, Aravind; Rogers, Baxter P.; Hohman, Tim; Jefferson, Angela L.; Shashikumar, Niranjana; Pechman, Kimberly R.; Davis, L. Taylor; Moyer, Daniel; Schilling, Kurt G.; Archer, Derek; Anderson, Adam; Landman, Bennett A. (2026). . PLOS ONE, 21(7), e0350808.
Diffusion tensor imaging (DTI) is an MRI technique used to study the structure of brain tissue, particularly white matter. However, imperfections in the MRI system鈥檚 magnetic field gradients can slightly alter diffusion measurements across different parts of the brain and may bias results if not corrected. This study examined the effects of correcting these gradient nonlinearities (GNL) and whether they could influence conclusions about aging and neurological conditions. We analyzed 948 imaging sessions from the 91吃瓜网 Memory & Aging Project, including measurements of white and gray matter. GNL correction produced changes of about 1% in fractional anisotropy and 3.3% in mean diffusivity, two measures of brain tissue microstructure, as well as changes of about 5 degrees in the estimated direction of nerve fibers, affecting at least 20% of the brain. Some brain regions, particularly superior, occipital, and parietal areas, were more affected, while larger-scale structural measurements changed by up to 12%. Although the effects were generally small at the individual level, they could become statistically important in large studies, particularly those involving multiple MRI scanners or sites. These findings suggest that GNL effects should be considered and, when possible, corrected or measured in large brain imaging studies to improve the reliability of comparisons across individuals, scanners, and clinical groups.
Fig 1.聽(a) The MCI clinical cohort comprises 327 participants, each undergoing up to four diffusion MRI sessions acquired on Scanner A (blue) or Scanner B (green).
(b) Maps of the diagonal elements of the gradient nonlinearity tensor, Lxx, Lyy, and Lzz, estimated from empirical phantom field maps for each scanner (FOV 384鈥壝椻��384鈥壝椻��384 mm). These entries describe the local scaling of nominal gradients applied along the x, y, and z axes and illustrate scanner-dependent spatial variation in effective diffusion weighting. (c) At a representative brain location (marked in panel b), the sphere plot shows the angular deviation between nominal and GNL-corrected b-vectors (example shown for Scanner B), and the line plot shows the corresponding effective b-values across diffusion volumes for Scanner A (blue) and Scanner B (green) relative to the nominal b-value (orange). Together, these panels summarize the cohort, the underlying gradient nonlinearity fields, and their impact on the effective diffusion encoding used in our analyses. Our study analyzes the effects of GNL on this clinical cohort.