Automated image registration: I. General methods and intrasubject, intramodality validation.
OBJECTIVE
We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities.


METHODS
Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data.


RESULTS
All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy.


CONCLUSIONS
The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.
Woods
R P
RP
Division of Brain Mapping, UCLA School of Medicine, USA.
Grafton
S T
ST
Holmes
C J
CJ
Cherry
S R
SR
Mazziotta
J C
JC
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008432:Mathematical Computing; D019047:Phantoms, Imaging; D011381:Programming Languages; D012016:Reference Values; D015203:Reproducibility of Results; D014055:Tomography, Emission-Computed