Assessing DTI data quality using bootstrap analysis

Diffusion tensor imaging (DTI) is an established method for characterizing and quantifying ultrastructural brain tissue properties. However, DTI-derived variables are affected by various sources of signal uncertainty. The goal of this study was to establish an objective quality measure for DTI based on the nonparametric bootstrap methodology. The confidence intervals (CIs) of white matter (WM) fractional anisotropy (FA) and Clinear were determined by bootstrap analysis and submitted to histogram analysis. The effects of artificial noising and edge-preserving smoothing, as well as enhanced and reduced motion were studied in healthy volunteers. Gender and age effects on data quality as potential confounds in group comparison studies were analyzed. Additional noising showed a detrimental effect on the mean, peak position, and height of the respective CIs at 10% of the original background noise. Inverse changes reflected data improvement induced by edge-preserving smoothing. Motion-dependent impairment was also well depicted by bootstrap-derived parameters. Moreover, there was a significant gender effect, with females displaying less dispersion (attributable to elevated SNR). In conclusion, the bootstrap procedure is a useful tool for assessing DTI data quality. It is sensitive to both noise and motion effects, and may help to exclude confounding effects in group comparisons.