Automated image registration: II. Intersubject validation of linear and nonlinear models.
OBJECTIVE
Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity.


METHODS
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.


CONCLUSIONS
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.
Woods
R P
RP
Department of Neurology, UCLA School of Medicine, USA.
Grafton
S T
ST
Watson
J D
JD
Sicotte
N L
NL
Mazziotta
J C
JC
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping; D005260:Female; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D012016:Reference Values; D015203:Reproducibility of Results; D014055:Tomography, Emission-Computed