title: CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation.
abstract: Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.
authors.affiliation: Department of Pediatric Neurology and Developmental Medicine, Children's Hospital and Experimental Pediatric Neuroimaging Group, Children's Hospital and Department of Neuroradiology, University of Tübingen Tübingen, Germany.
authors.forename: Marko
authors.initials: M
authors.lastname: Wilke
authors.affiliation: Pediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Pediatrics, Division of Biostatistics and Epidemiology, University of Cincinnati College of Medicine Cincinnati, OH, USA.
authors.forename: Mekibib
authors.initials: M
authors.lastname: Altaye
authors.affiliation: Pediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Radiology, University of Cincinnati College of Medicine Cincinnati, OH, USA.
authors.forename: Scott K
authors.initials: SK
authors.lastname: Holland
keywords: MRI template; multivariate adaptive regression splines; pediatric neuroimaging; spatial normalization; spline interpolation