Respiratory Noise Correction Using Linear Phase Regression (RCP)

Hu Cheng¹, Yu Li²

Respiratory noise is a confounding factor in functional MRI data analysis. A novel method is proposed to retrospectively correct for the respiratory noise in fMRI data using linear regression of the phases from different slices. This method can effectively remove noise that correlates with the respiration. This new method is compared with RETROICOR, which requires recording respiration signal simultaneously in an fMRI experiment. The two techniques show comparable performance with respect to the respiratory noise correction for fMRI time series.