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 C linear were determined by bootstrap analysis and submitted to histogram analysis. The effects of artificial noising and edgepreserving 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. Key words: bootstrap; DTI quality; anisotropy; reliability; motion; noise Diffusion tensor imaging (DTI) is susceptible to numerous detrimental artifacts that may impair the reliability and validity of the obtained data (1). Thermal noise, eddy currents, susceptibility artifacts, rigid body motion, physiological pulsation flow, and hardware issues (e.g., gradient miscalibration) contribute significantly to the resulting overall noise.While considerable research has focused on the analysis and modeling of some of these influencing factors (2-4), and the development of respective correction methods (5-7), relatively little effort has been spent on estimating the residual error in real data (8,9).A major consequential problem is the uncontrolled propagation into derived parameters (4,8) causing both random and systematic errors. In practical terms, this particularly affects the reliability of results from fiber tractography (10,11), anisotropy estimates, and, more generally, any inferences from group comparisons that may be impaired by nonidentically distributed data quality.Therefore, objective measures to assess the quality of an individual data set are urgently needed. To date, several attempts have been made to determine the uncertainty in DTI data by specific metrics (e.g., the motion artifact index (12) or a fitting error-based parameter (9)). The signal-tonoise ratio (SNR) is the most commonly used measure of data quality; however, it is not unequivocally defined, and is highly dependent on details of the NMR diffusion sequence and encoding scheme used (2,13). A further drawback is the yet unknown sensitivity of SNR to motion artifacts, whi...

Summary. The empirical origin of random noise is described, its influence on DTI variables is illustrated by a review of numerical and in vivo studies supplemented by new simulations investigating high noise levels. A stochastic model of noise propagation is presented to structure noise impact in DTI. Finally, basics of voxelwise and spatial denoising procedures are presented. Recent denoising procedures are reviewed and consequences of the stochastic model for convenient denoising strategies are discussed.

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