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

Purpose: The 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 methods: Eight 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/mm(2) and 1500 s/mm(2). The number of diffusion gradient directions (N(d)) was reduced from the original value whilst maintaining a homogeneous gradient distribution enabling direct comparison of subsampled data sets with N(d)=15, 28, 43, 84, 112 and 127. FA and MD maps were generated and analyzed using tract-based spatial statistics. Effect of N(d) on estimated FA and MD was tested with voxel-wise statistics in 13 regions of interest. The number of voxels supporting two fiber populations (NV(2)) at different N(d) values was estimated using Bayesian estimation of diffusion parameters.

Results: Low FA values decreased significantly with increasing N(d) and with increasing NSA. MD was only marginally sensitive to N(d) and NSA. NV(2) increased significantly with N(d) 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.