Automated image registration: I. General methods and intrasubject, intramodality validation
Related Experiment Video
Updated: Oct 6, 2026
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
R P Woods1, S T Grafton, C J Holmes
1Division of Brain Mapping, UCLA School of Medicine, USA.
Purpose:
We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities.
Method:
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.
Conclusion:
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.
