Friere_RegistrationSimilarityMeasures - 470 IEEE...

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470 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 21, NO. 5, MAY 2002 What is the Best Similarity Measure for Motion Correction in fMRI Time Series? L. Freire*, A. Roche, and J.-F. Mangin Abstract— It has been shown that the difference of squares cost function used by standard realignment packages (SPM and AIR) can lead to the detection of spurious activations, because the motion parameter estimations are biased by the activated areas. Therefore, this paper describes several experiments aiming at selecting a better similarity measure to drive functional magnetic resonance image registration. The behaviors of the Geman–Mc- Clure (GM) estimator, of the correlation ratio, and of the mutual information (MI) relative to activated areas are studied using simulated time series and actual data stemming from a 3T magnet. It is shown that these methods are more robust than the usual difference of squares measure. The results suggest also that the measures built from robust metrics like the GM estimator may be the best choice, while MI is also an interesting solution. Some more work, however, is required to compare the various robust metrics proposed in the literature. Index Terms— Artifact, fMRI, motion correction, robust regis- tration, spurious activation. I. INTRODUCTION M OTION correction in functional magnetic resonance imaging (fMRI) time series is usually performed through the retrospective estimation of the subject’s motion during the experiment. This motion is often modeled by a time series of three–dimensional (3-D) rigid body transformations, each transformation aligning one volume of the time series with the reference volume. The retrospective approach amounts then to the estimation of these transformations via the maximization of a similarity measure. A number of different similarity mea- sures have been proposed in the literature in order to perform retrospective registration of 3-D data sets. Hence, a usual issue for the design of a registration procedure is the choice of the best similarity measure considering the characteristics of the problem being addressed [1]. This choice, indeed, may highly influence the procedure robustness and accuracy. This paper is dedicated to various experiments aiming at selecting the best similarity measure for motion correction in fMRI time series. Realignment of fMRI time-series is today considered as a re- quired preprocessing step before analysis of functional activa- Manuscript received November 9, 2001; revised February 25, 2002. Asterisk indicates corresponding author. *L. Freire is with the Service Hospitalier Frédéric Joliot, CEA, 91401 Orsay, France, Instituto de Biofísica e Engenharia Biomédica, FCUL, 1749-016 Lisboa, Portugal, and the Instituto de Medicina Nuclear, FML, 1649-028 Lisboa, Portugal (e-mail: [email protected]). A. Roche is with the Epidaure project, INRIA, Sophia Antipolis, France and
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Friere_RegistrationSimilarityMeasures - 470 IEEE...

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