Modeling and suppression of respiration-related physiological noise in echo-planar functional magnetic resonance imaging using global and one-dimensional navigator echo correction

A major source of noise in functional magnetic resonance imaging (fMRI) arises from modulations in the local magnetic field in the head due to motion of the subject's chest through the respiratory cycle, and this physiologic noise can nullify the gains in statistical power expected by the use of higher magnetic fields for fMRI. In particular, fMRI data acquired using echo-planar imaging (EPI) are very sensitive to these spatially and temporally varying respiration-induced frequency offsets. In this study, accurate 3D magnetic field maps in the head were measured and used to determine the frequency offsets at the two extremes of the respiratory cycle. From these maps, spatially dependent frequency variations from about -1.0 Hz to +1.5 Hz were measured in the brain through the respiratory cycle. Simulations of a typical axial EPI fMRI experiment acquired in the presence of this measured field variation were performed, demonstrating regional image intensity variations between 1 and 5% in single pixel time series. The inadequacy of either global or 1 D navigator echo corrections to measure and suppress respiratory-induced noise in fMRI time series is demonstrated. The nature of the spatial variations observed suggests that 2D approaches should be considered.