In this talk, I will introduce the basics of image registration and motion correction. I will then compare the hypotheses underlying the classical similarity measures used to drive
registration, with a focus on their behaviour with fMRI time series. I will show that the motion correction methods used by the brain mapping community may sometimes induce spurious activations in some motion-free fMRI studies. This artefact stems from the fact that activated areas behave like biasing outliers for the
difference of squares based measure used by these methods. I will conclude with a parallel between motion correction in fMRI and distortion correction in diffusion-weighted time series.
L. Freire, A. Roche, and J.-F. Mangin. What is the best similarity measure for motion correction in fMRI time series?. IEEE Trans. Medical Imaging,
21(5):470--484, May 2002.
L. Freire and J.-F. Mangin. Motion correction algorithms may create spurious brain activations in the absence of subject motion. Neuroimage, 14(3):709--722, 2001.
J.-F. Mangin, C. Poupon, C. A. Clark, D. Le Bihan, and I. Bloch. Distortion correction and robust tensor estimation for MR diffusion imaging. Medical Image Analysis, 6:191--198, 2002
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