Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation

Woods, Roger P., Grafton, Scott T., Holmes, Colin J., Cherry, Simon R., Mazziotta, John C.·1998·Journal of Computer Assisted Tomography

DOI 10.1097/00004728-199801000-00027

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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 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.

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Authors: Woods, R. P., Grafton, S. T., Holmes, C. J., Cherry, S. R., & Mazziotta, J. C.
Venue: Journal of Computer Assisted Tomography
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 1998-01
Volume: 22
Issue: 1
Pages: 139-152
ISSN: 0363-8715 (ISSN-L)
DOI: 10.1097/00004728-199801000-00027
OpenAlex: W2104612543
Cited by: 1,707
References: 0
FWCI: 108.42
Type: journal article
Language: en
Open access: closed

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Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation

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