Unsupervised physiological noise correction of functional magnetic resonance imaging data using phase and magnitude information (PREPAIR)

• Published in: Human brain mapping Vol. 44; no. 3; pp. 1209 - 1226
• Main Authors: Bancelin, David, Bachrata, Beata, Bollmann, Saskia, Lima Cardoso, Pedro, Szomolanyi, Pavol, Trattnig, Siegfried, Robinson, Simon Daniel
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.02.2023
• Subjects: Aliasing; Blood vessels; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Echo-Planar Imaging; Fluctuations; fMRI; Functional magnetic resonance imaging; Heart; Humans; Magnetic fields; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Methods; noise correction; phase data; physiological noise; Physiology; PREPAIR; Resonance; Respiration; Scanners; Sensitivity; Signal processing; Signal to noise ratio; Statistical power; Time series; unsupervised; fMRI; physiological noise; PREPAIR; noise correction; unsupervised; phase data
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.26152

Of the sources of noise affecting blood oxygen level‐dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data‐driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo‐planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub‐tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording‐free physiological noise correction tools—PESTICA and FIX, both performed in unsupervised mode—PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal‐to‐noise‐ratio at both 3 and 7 T. We have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo‐planar imaging data. We demonstrate that the physiological signal time courses identified with Physiological Regressor Estimation from Phase and mAgnItude, sub‐tR (PREPAIR) not only agree well with those from external devices, but also retrieve challenging cardiac dynamics.