This work develops a tractography algorithm to leverage fibre dispersion estimates derived from fitting parametric models of orientation dispersion to diffusion data. Tractography techniques are powerful tools to probe white matter (WM) connectivity non-invasively. Most current techniques follow a small number of discrete directions per voxel to identify WM connections. This approach addresses the limitation of traditional DTI-based tractography for regions with crossing fibres. However, it remains an oversimplification for regions with fanning and bending configurations, where the underlying fibre orientation distributions are continuous rather than discrete [2]. Following only a discrete set of directions in this case misrepresents the underlying anatomy and is likely to result in false negative connectivity estimates. Recent parameterized models of fibre dispersion represent such sub-voxel fibre architecture more realistically and provide more accurate estimates of dispersion than non-parametric techniques such as spherical deconvolution, which are vulnerable to noise [3]. Here, we present a new tractography algorithm, hereby referred to as ND-Track (Neurite Dispersion Tracking), that leverages directional information gathered from parametric models of dispersion. We investigate the advantages of tracking with dispersion measures on a simple phantom and in in-vivo data, tracking through the coronal radiata, a region known to exhibit a significant degree of fibre dispersion. We further demonstrate that this approach does not compromise the tracking of the WM pathways for which the standard technique works well
