  Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.
