A probabilistic approach for estimation of physiological movements in fMRI

In functional magnetic resonance images (fMRI), to accurately detect functional activation areas, it is necessary to eliminate the physiological movements of subject, which cause false activation areas, from fMRI time series data. This paper proposes a method for estimation of not only rigid-body motion such as gross head motion, but also non-rigid-body motion like pulsatile blood and cerebrospinal fluid (CSF) flow. Our method estimates these types of movements by using optical flow on a pixel-by-pixel basis. We extend generalized gradient schemes in order to compute probability distributions of optical flow. The crux of our method is to compute optical flow on a pixel-by-pixel basis. Although many other methods assume that the subject movement is rigid-body motion, our method does not require this assumption. We demonstrate that the detection of brain activation areas can be improved by motion correction based on our method.