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

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 establi...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Fla.), 2013-08, Vol.76, p.400-411
Main Authors:	de Groot, Marius, Vernooij, Meike W., Klein, Stefan, Ikram, M. Arfan, Vos, Frans M., Smith, Stephen M., Niessen, Wiro J., Andersson, Jesper L.R.
Format:	Article
Language:	eng
Subjects:	Algorithms Attention Deficit Hyperactivity Disorder Automation Biological and medical sciences Brain Mapping - methods Diffusion imaging Diffusion Tensor Imaging - methods Elastix Evaluation FNIRT Fundamental and applied biological sciences. Psychology Humans Image Interpretation, Computer-Assisted - methods Optimization Registration Statistics TBSS Vertebrates: nervous system and sense organs
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Summary:	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 (R2 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. •FA registration performance was evaluated using probabilistic tractography.•FNIRT and Elastix nonlinear registration algorithms were optimized on 2 FA datasets.•High-dimensional warps outperformed registration performance of TBSS v1.2.•TBSS can be improved; replace skeleton-projection by high-dimensional registration
ISSN:	1053-8119 1095-9572