Distortion correction via non-rigid registration of functional to anatomical magnetic resonance brain images

Ali Gholipour, Nasser Kehtarnavaz, Kaundinya Gopinath, Richard Briggs, Michael Devous, Robert Haley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Functional to anatomical brain image registration is needed for accurate localization of brain activation maps. Due to the presence of nonlinear distortions, it is more effective to consider non-rigid transformations to achieve such a registration. In this paper, a non-rigid registration technique based on the B-spline free-form deformation model and mutual information similarity measure is introduced. An optimization formulation is devised to achieve a fast and robust registration. This formulation differs from the previous formulations by utilizing a limited-memory, second-order optimization algorithm instead of the usual first-order gradient-based algorithms. It also enforces hard parameter constraints instead of constraints based upon physics or Jacobian smoothness. The results obtained indicate that this registration technique provides improvements over rigid and affine techniques when registering functional to anatomical magnetic resonance brain images.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages1181-1184
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
Country/TerritoryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

Keywords

  • Brain
  • Distortion
  • Image registration
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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