Automated brainstem co-registration (ABC) for MRI

Group data analysis in brainstem neuroimaging is predicated on accurate co-registration of anatomy. As the brainstem is comprised of many functionally heterogeneous nuclei densely situated adjacent to one another, relatively small errors in co-registration can manifest in increased variance or decreased sensitivity (or significance) in detecting activations. We have devised a 2-stage automated, reference mask guided registration technique (Automated Brainstem Coregistration, or ABC) for improved brainstem co-registration. Our approach utilized a brainstem mask dataset to weight an automated coregistration cost function. Our method was validated through measurement of RMS error at 12 manually defined landmarks. These landmarks were also used as guides for a secondary manual coregistration option, intended for outlier individuals that may not adequately co-register with our automated method. Our methodology was tested on 10 healthy human subjects and compared to traditional co-registration techniques (Talairach transform and automated affine transform to the MNI-152 template). We found that ABC had a significantly lower mean RMS error (1.22 +/- 0.39 mm) than Talairach transform(2.88 +/- 1.22 mm, mu +/- sigma) and the global affine (3.26 +/- 0.81 mm) method. Improved accuracy was also found for our manual-landmark-guided option (1.51 +/- 0.43 mm). Visualizing individual brainstem borders demonstrated more consistent and uniform overlap for ABC compared to traditional global co-registration techniques. Improved robustness (lower susceptibility to outliers) was demonstrated with ABC through lower inter-subject RMS error variance compared with traditional co-registration methods. The use of easily available and validated tools (AFNI and FSL) for this method should ease adoption by other investigators interested in brainstem data group analysis. (c) 2006 Elsevier Inc. All rights reserved.