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Contour‐based registration technique to differentiate between task‐activated and head motion‐induced signal variations in fMRI
Author(s) -
Biswal Bharat B.,
Hyde James S.
Publication year - 1997
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910380315
Subject(s) - artificial intelligence , computer science , computer vision , functional magnetic resonance imaging , signal (programming language) , intensity (physics) , head (geology) , magnetic resonance imaging , motion (physics) , imaging phantom , pattern recognition (psychology) , task (project management) , physics , neuroscience , optics , medicine , geology , psychology , management , geomorphology , economics , radiology , programming language
Abstract Data acquired using functional magnetic resonance imaging are often contaminated by head motion. As a result, optimal information regarding task‐induced (or resting‐state) signal changes cannot be extracted. Intensity‐based registration methods, including intensity correlation or minimum intensity variance techniques, are widely used to register two or more images. It is shown here that intensity‐based registration cannot accurately register two or more images in the presence of local intensity changes arising from functional magnetic resonance, fMRI, signals. In this paper, we present a contour‐based technique that can be used not only for a more robust registration, but also to help differentiate between task‐induced and motion‐induced signal changes. Results obtained using both phantom and human brain images demonstrate advantages of this technique compared with a conventional intensity registration technique.