Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland.
Neuroimage. 2012 Apr 2;60(2):1517-27. doi: 10.1016/j.neuroimage.2012.01.067. Epub 2012 Jan 18.
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.
在本文中，我们介绍了 DRIFTER 算法，这是一种新的基于贝叶斯的模型，用于从功能磁共振成像 (fMRI) 数据中回溯消除生理噪声。在该方法中，我们首先使用交互多模型 (IMM) 滤波器算法估计生理信号的频率轨迹。这些频率轨迹可以从外部参考信号中估计，或者如果时间分辨率足够高，也可以从 fMRI 数据中估计。然后，将估计的频率轨迹与卡尔曼滤波器 (KF) 和 Rauch-Tung-Striebel (RTS) 平滑器相结合，在状态空间模型中使用，该模型将信号分为与激活相关的清洁信号、生理噪声和白色测量噪声分量。使用实验数据，我们表明，如果生理信号的形状和幅度随时间变化，该方法优于 RETROICOR 算法。
