Automated image registration: I. General methods and intrasubject, intramodality validation

RP Woods, ST Grafton, CJ Holmes… - Journal of computer …, 1998 - journals.lww.com
RP Woods, ST Grafton, CJ Holmes, SR Cherry, JC Mazziotta
Journal of computer assisted tomography, 1998journals.lww.com
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 …
Abstract
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 μm 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.
Lippincott Williams & Wilkins