AUTOMATED IMAGE REGISTRATION - I - GENERAL-METHODS AND INTRASUBJECT, INTRAMODALITY VALIDATION

Purpose: We sought to describe and validate an automated image registr ation 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 c onsistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phanto m for PET data. Results: All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accur acy of registration of structural MRI images was in the 75 to 150 mu m range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. Conclusion: The registratio n algorithm described is a robust and flexible tool that can be used t o address a variety of image registration problems. Registration strat egies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.