Automated image registration: II. Intersubject validation of linear and nonlinear models
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Updated: Oct 30, 2026
Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
Published on: October 27, 2023
R P Woods1, S T Grafton, J D Watson
1Department of Neurology, UCLA School of Medicine, USA.
Purpose:
Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity.
Method:
PET and MRI data from 22 normal subjects were registered to corresponding averaged PET or MRI brain atlases using several specific linear and nonlinear spatial transformation models with an automated algorithm. Validation was based on anatomically defined landmarks.
Results:
Automated registration produced results that were superior to a manual nine parameter variant of the Talairach registration method. Increasing the degrees of freedom in the spatial transformation model improved the accuracy of automated intersubject registration.
Conclusion:
Linear or nonlinear automated intersubject registration based on voxel intensities is computationally practical and produces more accurate alignment of homologous landmarks than manual nine parameter Talairach registration. Nonlinear models provide better registration than linear models but are slower.
