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

Roger P. Woods, Scott T. Grafton, Colin J. Holmes, Simon R Cherry, John C. Mazziotta

Research output: Contribution to journalArticle

1538 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)139-152
Number of pages14
JournalJournal of Computer Assisted Tomography
Volume22
Issue number1
DOIs
StatePublished - 1998

Keywords

  • Brain mapping
  • Emission computed tomography
  • Image registration
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Fingerprint Dive into the research topics of 'Automated image registration: I. General methods and intrasubject, intramodality validation'. Together they form a unique fingerprint.

  • Cite this