Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER

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Journal Title
Journal ISSN
Volume Title
School of Electrical Engineering | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2012
Major/Subject
Mcode
Degree programme
Language
en
Pages
1517-1527
Series
NeuroImage, Volume 60, Issue 2
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.
Description
Keywords
Functional magnetic resonance imaging, Physiological noise, Kalman filter, RTS smoother, Interacting multiple models, Bayesian inference
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Citation
Särkkä, Simo & Solin, Arno & Nummenmaa, Aapo & Vehtari, Aki & Auranen, Toni & Vanni, Simo & Lin, Fa-Hsuan. 2012. Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. NeuroImage. Volume 60, Issue 2. 1517-1527. ISSN 1053-8119 (printed). DOI: 10.1016/j.neuroimage.2012.01.067.