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Tractography algorithms are used extensively to delineate white matter structures, by operating on the voxel-wise information generated through the application of diffusion tensor imaging (DTI) or other models to diffusion weighted (DW) magnetic resonance imaging (MRI) data. We demonstrate that these methods commonly yield systematic streamline length dependent distortions of tractography derived tissue microstructure parameters, such as fractional anisotropy (FA). This dependency may be described as piecewise linear. For streamlines shorter than an inflection point (determined for a group of tracts delineated for each individual brain), estimates of tissue microstructure exhibit a positive linear relation with streamline length. For streamlines longer than the point of inflection, the association is weaker, with the slope of the relationship between streamline length and tissue microstructure differing only marginally from zero. As the dependency is most pronounced for a range of streamline lengths encountered typically in DW imaging of the human brain (less than ~100 mm), our results suggest that some previous estimates of tissue microstructure should be treated with considerable caution. A method is described, whereby an Akaike information weighted average of linear, Blackman and piecewise linear model predictions, may be used to compensate effectively for the dependence of FA (and other estimates of tissue microstructure) on streamline length, across the entire range of streamline lengths present in each specimen.