Motion Correction
Jean-Francois Mangin
CEA Saclay, France
In this talk, I introduce the basics of image registration and motion correction. I then compare the assumptions underlying classical similarity measures used to guide registration, with particular emphasis on their behavior in fMRI time series. I demonstrate that motion correction methods commonly used in the brain mapping community may sometimes induce spurious activations in motion-free fMRI studies.
This artifact arises because activated regions can behave as biasing outliers for the difference-of-squares similarity measure employed by these methods. I conclude by drawing a parallel between motion correction in fMRI and distortion correction in diffusion-weighted time series.
References:
L. Freire, A. Roche, and J.-F. Mangin. “What is the best similarity measure for motion correction in fMRI time series?” IEEE Transactions on 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.
