Implementation of the bootstrap procedure results in automated quantitative assessment of data quality. Beyond, this approach turns out to be highly sensitive. Quality impairment due to gross errors, such as faulty acquisition evolved from technical issues or crude motion during the scanning time, is successfully identified. Gradual noise corruption of the magnitude signal is directly linked to increasing mean width of the confidence intervals for FA in white matter voxels. On the other hand, the application of bootstrap method proved the potential of a nonlinear smoothing technique to recover image quality.