  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.
