title: Brain atlases and neuroanatomic imaging.
abstract: Quantifying the effect of a genetic manipulation or disease is a complicated process in a population of animals. Probabilistic brain atlases can capture population variability and be used to quantify those variations in anatomy as measured by structural imaging. Minimum deformation atlases (MDAs), a subclass of probabilistic atlases, are intensity-based averages of a collection of scans in a common space unbiased by selection of a single target image. Here, we describe a method for generating an MDA from a set of magnetic resonance microscopy images. First, the images are segmented to remove any non-brain tissue and bias field corrected to remove field inhomogeneities. The corrected images are then linearly aligned to a representative scan, the geometric mean of all the transformations is calculated, and a minimum deformation target (MDT) is produced by averaging the volumes in this new space. The brains are then non-linearly aligned to the MDT to produce the MDA. Finally, the images are linearly aligned to the MDA using a full-affine transformation to spatially and intensity normalize them, removing global differences in size, shape, and position but retaining anatomically significant differences.
authors: Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA, USA.
authors: Allan
authors: A
authors: MacKenzie-Graham
authors: Jyl
authors: J
authors: Boline
authors: Arthur W
authors: AW
authors: Toga
mesh_terms: D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D009445:Neuroanatomy