title: Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland. simo.sarkka@aalto.fi
authors.forename: Simo
authors.initials: S
authors.lastname: Särkkä
authors.forename: Arno
authors.initials: A
authors.lastname: Solin
authors.forename: Aapo
authors.initials: A
authors.lastname: Nummenmaa
authors.forename: Aki
authors.initials: A
authors.lastname: Vehtari
authors.forename: Toni
authors.initials: T
authors.lastname: Auranen
authors.forename: Simo
authors.initials: S
authors.lastname: Vanni
authors.forename: Fa-Hsuan
authors.initials: FH
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010100:Oxygen / Q000097:blood
chemical_list: D010100:Oxygen