title: Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images.
abstract: Typical packages used for coregistration in functional image analyses include automated image registration (AIR) and statistical parametric mapping (SPM). However, both methods have limited-dimension deformation models. A fully deformable model, which combines the piecewise linear registration for coarse alignment with demons algorithm for voxel-level refinement, allows a higher degree of spatial deformation. This leads to a more accurate colocalization of the functional signal from different subjects and therefore can produce a more reliable group average signal. We quantitatively compared the performance of the three different registration approaches through a series of experiments and we found that the fully deformable model consistently produces a more accurate structural segmentation and a more reliable functional signal colocalization than does AIR or SPM.
authors.affiliation: Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
authors.forename: Minjie
authors.initials: M
authors.lastname: Wu
authors.forename: Owen
authors.initials: O
authors.lastname: Carmichael
authors.forename: Pilar
authors.initials: P
authors.lastname: Lopez-Garcia
authors.forename: Cameron S
authors.initials: CS
authors.lastname: Carter
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Aizenstein
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D001270:Cervical Atlas; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D012680:Sensitivity and Specificity