Abstract
Physiological noise, if unaccounted for, can drastically reduce the statistical significance of detected activation in FMRI. In this paper, we systematically optimize physiological noise regressions for multi-shot 3D FMRI data. First, we investigate whether 3D FMRI data are best corrected in image space (RetroICor) or k-space (RetroKCor), in which each k-space segment can be assigned its unique physiological phase. In addition, the optimal regressor set is determined using the Bayesian Information Criterion (BIC) for a variety of 3D acquisitions corresponding to different image contrasts and k-space readouts. Our simulations and experiments indicate that: (a) k-space corrections are more robust when performed on real/imaginary than magnitude/phase data; (b) k-space corrections do not outperform image-space corrections, despite the ability to synchronize physiological phase to acquisition time more accurately; and (c) the optimal model varied considerably between the various acquisition techniques. These results suggest the use of a tailored set of volume-wide regressors, determined by BIC or other selection criteria, that achieves optimal balance between variance reduction and potential over-fitting.