Journal
NEUROIMAGE
Volume 60, Issue 2, Pages 1517-1527Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2012.01.067
Keywords
Functional magnetic resonance imaging; Physiological noise; Kalman filter; RTS smoother; Interacting multiple models; Bayesian inference
Funding
- United States National Institutes of Health (NIH) [R01HD040712, R01NS037462, R01NS048279, P41RR014075, R01MH083744, R21DC010060, R21EB007298]
- National Science Council, Taiwan [NSC 98-2320-B-002-004-MY3, NSC 100-2325-B-002-046]
- National Health Research Institute, Taiwan [NHRI-EX100-9715EC]
- Academy of Finland [124698, 125349, 127624, 129670, 218054, 218248]
- National Center for Research Resources
- Academy of Finland (AKA) [127624, 125349, 218054, 125349] Funding Source: Academy of Finland (AKA)
Ask authors/readers for more resources
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. (c) 2012 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available