TBSS++: A novel computational method for Tract-Based Spatial Statistics

TL;DR

TBSS++ introduces an advanced computational framework that enhances the accuracy and reproducibility of cross-subject white matter analysis in dMRI by utilizing fiber orientation-based registration and detailed tract atlases.

Contribution

It presents a novel method that overcomes TBSS limitations through improved tract segmentation and registration using fiber orientation distributions and tract atlases.

Findings

Higher reproducibility compared to TBSS

Greater robustness to data perturbations

Significant improvement in cross-subject analysis accuracy

Abstract

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are compared between cohorts of subjects. The accuracy and reliability of these studies hinges on the ability to compare precisely the same white matter tracts across subjects. This is an intricate and error-prone computation. Existing computational methods such as Tract-Based Spatial Statistics (TBSS) suffer from a host of shortcomings and limitations that can seriously undermine the validity of the results. We present a new computational framework that overcomes the limitations of existing methods via (i) accurate segmentation of the tracts, and (ii) precise registration of data from different subjects/scans. The registration is based on fiber orientation distributions. To further improve the alignment of cross-subject data, we create detailed atlases of white matter tracts. These atlases serve as an unbiased reference space where the data from all subjects is registered for comparison. Extensive evaluations show that, compared with TBSS, our proposed framework offers significantly higher reproducibility and robustness to data perturbations. Our method promises a drastic improvement in accuracy and reproducibility of cross-subject dMRI studies that are routinely used in neuroscience and medical research.