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

Journal article

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.

Authors: de Groot, M; Vernooij, M; Klein, S; Ikram, M; Vos, F

• Publication status: Published
• Journal: NeuroImage
• Volume: 76
• Issue: 1
• Pages: 400-411
• Publication date: 2013-08-01
• DOI: 10.1016/j.neuroimage.2013.03.015
• EISSN: 1095-9572
• ISSN: 1053-8119
• Language: English
• Keywords: Image Interpretation, Computer-Assisted; Humans; Algorithms; Diffusion Tensor Imaging; Brain Mapping