Retrospective processing of DTI tractography to compensate for partial volume effects

Partial volume effects are one of the most common sources of error in diffusion tensor imaging (DTI) tractography. For example, in data from older subjects or Alzheimer's disease probable subjects, the situation is especially exacerbated around the dilated ventricle, which causes erroneous merging of tracts. Rescanning the subject at higher resolution is the best solution, but often times unattainable. We offer a retrospective filtering algorithm, which is purely subtractive, based on a region of interest (ROI) filtering methodology that filters tracts by their shape and seed points. The ROIs are defined using both anatomic images and fractional anisotropy (FA) maps in normalized space allowing for consistency across all subjects. Our algorithm helps correct the partial volume effects by reducing the overestimation of tract length, giving a more accurate regional tract count. The objective of our retrospective algorithm is reclamation of data sets from partial volume effects.