Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and...

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.