The effect of gradient sampling schemes on diffusion metrics derived from probabilistic analysis and tract-based spatial statistics

Abstract

PurposeThe purpose was to systematically evaluate the effect of diffusion gradient encoding scheme on estimated fractional anisotropy (FA), mean diffusivity (MD) and the voxel-wise probability of identifying crossing fibers in the brain. Materials and MethodsEight healthy volunteers (mean age 26.5±1.3 years, 5 males, 3 females) were imaged using a Spin-Echo Echo-Planar-Imaging sequence acquired with two signal averages [number of signals averaged (NSA)], 127 diffusion directions, and b-values of 750 s/mm2 and 1500 s/mm2. The number of diffusion gradient directions (Nd) was reduced from the original value whilst maintaining a homogeneous gradient distribution enabling direct comparison of subsampled data sets with Nd=15, 28, 43, 84, 112 and 127. FA and MD maps were generated and analyzed using tract-based spatial statistics. Effect of Nd on estimated FA and MD was tested with voxel-wise statistics in 13 regions of interest. The number of voxels supporting two fiber populations (NV2) at different Nd values was estimated using Bayesian estimation of diffusion parameters. ResultsLow FA values decreased significantly with increasing Nd and with increasing NSA. MD was only marginally sensitive to Nd and NSA. NV2 increased significantly with Nd but not with NSA. Thus, we conclude that accurate estimation of standard diffusion metrics FA and MD is mainly dependent on the signal-to-noise ratio (SNR), whereas the ability to differentiate multiple fiber populations requires a high diffusion sampling density.