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.
Original language | English (US) |
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Pages (from-to) | 747-754 |
Number of pages | 8 |
Journal | Human Brain Mapping |
Volume | 27 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2006 |
Keywords
- Atlas-based segmentation
- Deformable model
- fMRI
- Image registration
ASJC Scopus subject areas
- Clinical Neurology
- Neuroscience(all)
- Radiological and Ultrasound Technology