Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration

Abstract

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R(2) 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.

Tract‐specific white matter degeneration in aging: The Rotterdam Study

“…Furthermore, we took macrostructural white matter changes into account and assessed an independent association between age, cardiovascular risk factors, and microstructural organization. Finally, we performed tract-specific measurements of tissue microstructure using fully automated methods that we previously made publicly available [30], ensuring that our results can be used as reference values for other investigations.…”

“…PROBTRACKX uses a diffusion model estimated using BEDPOSTX, also available in FSL. Protocols for identifying 14 white matter tracts, 11 of which were defined for left and right hemispheres were defined as previously described [30] and were made available as the AutoPTX plugin for FSL 1 (version 0.1.1). Tracts were categorized in association, commissural, limbic and sensorimotor tracts.…”

“…To account for differences in geometric complexity, tract-specific thresholds were selected by optimizing the reproducibility of FA measurements on a subset of 30 participants who had been scanned twice, with a mean interval of 19.5 days (mean age 76.7 years (standard deviation 10.0)) [30]. Reproducibility and optimal thresholds are listed in Supplementary Table 1.…”

Longitudinal reliability of tract‐based spatial statistics in diffusion tensor imaging

“…Taken together this approach improves reliability and, in turn, improves the ability of TBSS to detect individual change. An improved registration procedure [e.g., de Groot et al, 2013] will likely address this aspect of variability with TBSS.…”

Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank imaging data

“…For T1-weighted data, segmentation of brain was conducted in two steps: firstly, a tissue-type segmentation using FAST (FMRIB's Automated Segmentation Tool) 25 was applied to extract cerebrospinal fluid, grey matter and white matter; then subcortical structures are extracted using FIRST (FMRIB's Integrated Registration and Segmentation Tool) 26 . For DTI data, parcellation of tracts were conducted using AutoPtx 27 . The summary data contained volumes of grey matter, white matter, cerebrospinal fluid, thalamus, putamen, pallidum, hippocampus, caudate, brain stem, amygdala and accumbens (Fig.…”