Reducing variability in along-tract analysis with diffusion profile realignment

Abstract

Diffusion weighted magnetic resonance imaging (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 human connectome project, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Independent 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.

Replication and generalization in applied neuroimaging

“…In all cases, the shape of the tract profiles remain similar across b-values. This supports the idea that tract profiles are a useful target for further investigation (St-Jean et al, 2019;Yeatman et al, 2014).…”

Evaluating the Reliability of Human Brain White Matter Tractometry

“…There are many aspects of reliability that could be further explored. We explored robustness with respect to ODF models and bundle recognition algorithms; robustness could also be explored with respect to data acquisition parameters within the same subject; preprocessing methods; profile extraction method (e.g., comparing our current approach with the BUndle ANalytics (BUAN) ( 56 )); and the effects of profile realignment on tract profile reliability ( 57 ). Another possibility for teasing apart measurement and tractography effects would be to test profile TRR using the streamline of one scan on the results of the second scan (by registering the streamline themselves, to avoid data interpolation in volume registration).…”

Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

“…Using the proposed approach, gradient nonlinearities can be accounted for, resulting in much more accurate apparent tissue densities. This could benefit studies employing apparent tissue densities obtained from data acquired with strong gradient amplitudes (Calamante, Jeurissen, Smith, Tournier, & Connelly, 2018; Chamberland et al, 2019; St‐Jean, Chamberland, Viergever, & Leemans, 2019).…”