Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers.

Abstract

BACKGROUND AND PURPOSE: Diffusion tensor MR imaging has the potential to improve our ability to monitor several neurologic conditions. As a preliminary step to the assessment of the role of diffusion tensor MR imaging in the context of longitudinal and multicenter studies, we evaluated the effect of sequence-, imaging unit-, and imaging-reimaging-induced variations on diffusion tensor MR imaging quantities derived from histogram analysis of a large portion of the central brain of healthy volunteers. METHODS: Each of eight healthy volunteers underwent imaging on two MR imaging units using three different pulsed gradient spin-echo single shot echo-planar pulse sequences (each of them having a different diffusion gradient scheme). Four additional healthy participants underwent imaging twice on the same imaging unit to assess imaging-reimaging variability. RESULTS: For mean diffusivity histograms, the differences between inter-sequence and inter-imaging unit coefficients of variation were significant for all the considered quantities with P values ranging from.003 to <.001. Also, the inter-imaging unit coefficient of variation for average fractional anisotropy was significantly higher than the corresponding inter-sequence coefficient of variation (P =.002). In general, inter-sequence mean diffusivity histogram-derived metrics (coefficients of variation ranging from 1.72% to 5.56%) were more reproducible than were fractional anisotropy histogram-derived metrics (coefficients of variation ranging from 5.45% to 7.34%). Imaging-reimaging variability was found to fall in the range of inter-sequence coefficients of variation for all the considered quantities. CONCLUSION: This study shows that inter-sequence, imaging-reimaging, and inter-imaging unit variabilities of diffusion tensor MR imaging-derived measurements are relatively low, suggesting that diffusion tensor MR imaging might provide additional measures of outcome with which to assess the evolution of brain structural damage in large scale studies of various neurologic conditions.