Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

作者：Sarkka Simo^*; Solin Arno; Nummenmaa Aapo; Vehtari Aki; Auranen Toni; Vanni Simo; Lin Fa Hsuan

来源：NeuroImage, 2012, 60(2): 1517-1527.

DOI：10.1016/j.neuroimage.2012.01.067

• Functional magnetic resonance imaging
• Physiological noise
• Kalman filter
• RTS smoother
• Interacting multiple models
• Bayesian inference

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.

• 出版日期2012-4-2