title: A Bayesian approach to the creation of a study-customized neonatal brain atlas.
abstract: Atlas-based image analysis (ABA), in which an anatomical "parcellation map" is used for parcel-by-parcel image quantification, is widely used to analyze anatomical and functional changes related to brain development, aging, and various diseases. The parcellation maps are often created based on common MRI templates, which allow users to transform the template to target images, or vice versa, to perform parcel-by-parcel statistics, and report the scientific findings based on common anatomical parcels. The use of a study-specific template, which represents the anatomical features of the study population better than common templates, is preferable for accurate anatomical labeling; however, the creation of a parcellation map for a study-specific template is extremely labor intensive, and the definitions of anatomical boundaries are not necessarily compatible with those of the common template. In this study, we employed a volume-based template estimation (VTE) method to create a neonatal brain template customized to a study population, while keeping the anatomical parcellation identical to that of a common MRI atlas. The VTE was used to morph the standardized parcellation map of the JHU-neonate-SS atlas to capture the anatomical features of a study population. The resultant "study-customized" T1-weighted and diffusion tensor imaging (DTI) template, with three-dimensional anatomical parcellation that defined 122 brain regions, was compared with the JHU-neonate-SS atlas, in terms of the registration accuracy. A pronounced increase in the accuracy of cortical parcellation and superior tensor alignment were observed when the customized template was used. With the customized atlas-based analysis, the fractional anisotropy (FA) detected closely approximated the manual measurements. This tool provides a solution for achieving normalization-based measurements with increased accuracy, while reporting scientific findings in a consistent framework.
authors.affiliation: Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Yajing
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA.
authors.forename: Linda
authors.initials: L
authors.lastname: Chang
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Can
authors.initials: C
authors.lastname: Ceritoglu
authors.affiliation: Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway.
authors.forename: Jon
authors.initials: J
authors.lastname: Skranes
authors.affiliation: Department of Medicine, School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Ernst
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
authors.affiliation: Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: koishi@mri.jhu.edu.
authors.forename: Kenichi
authors.initials: K
authors.lastname: Oishi
mesh_terms: D001271:Atlases as Topic; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D056324:Diffusion Tensor Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male
keywords: Customized atlas; MRI; Neonatal brain atlas; Registration accuracy; Volume-based template estimation (VTE)