Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI.
Evaluation of preprocessing steps to compensate for magnetic field distortions due to body movements in BOLD fMRI.
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DOI：
10.1016/j.mri.2009.07.005
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发表时间：
2010-02
影响因子：
2.5
通讯作者：
Menon, Ravi S.
中科院分区：
文献类型：
--
作者：
Barry, Robert L.;Williams, Joy M.;Klassen, L. Martyn;Gallivan, Jason P.;Culham, Jody C.;Menon, Ravi S.
关键词：
血氧水平依赖（BOLD）功能磁共振成像（fMRI）目前是脑功能无创研究的主要技术。BOLD fMRI面临的挑战之一，特别是在高场强下，是对由正常受试者呼吸以及在一些研究中受试者在扫描期间为执行与功能范式相关任务而移动所导致的时空变化磁场不均匀性（ΔB0）的影响进行补偿。数据采集过程中ΔB0的存在会使重建图像失真，并在fMRI时间序列中引入无关波动，从而降低BOLD对比度噪声比。优化fMRI数据处理流程以补偿几何畸变对于确保fMRI数据的高质量至关重要。为了研究由受试者移动引起的ΔB0，在有和没有模拟手臂同时运动的情况下收集了回波平面成像扫描数据。模拟手臂由实验者构建并移动，以模拟前臂运动，而受试者保持静止并观察视觉刺激范式。然后对这些数据进行八种不同组合的预处理步骤。最佳预处理流程包括导航校正、复相位回归因子和空间平滑。导航校正和相位回归之间的协同作用比单独使用任何一个步骤都能更好地减少几何畸变，并对数据进行预处理，使其更有利于空间平滑的效果。与仅使用导航校正和空间平滑相比，这些步骤的组合使t统计量提高了10%，并减少了在不应出现有效效应区域的噪声和假激活。
Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is currently the dominant technique for non-invasive investigation of brain functions. One of the challenges with BOLD fMRI, particularly at high fields, is compensation for the effects of spatiotemporally varying magnetic field inhomogeneities (ΔB0) caused by normal subject respiration, and in some studies, movement of the subject during the scan to perform tasks related to the functional paradigm. The presence of ΔB0 during data acquisition distorts reconstructed images and introduces extraneous fluctuations in the fMRI time series that decrease the BOLD contrast-to-noise ratio. Optimization of the fMRI data-processing pipeline to compensate for geometric distortions is of paramount importance to ensure high quality of fMRI data. To investigate ΔB0 caused by subject movement, echo-planar imaging scans were collected with and without concurrent motion of a phantom arm. The phantom arm was constructed and moved by the experimenter to emulate forearm motions while subjects remained still and observed a visual stimulation paradigm. These data were then subjected to eight different combinations of preprocessing steps. The best preprocessing pipeline included navigator correction, a complex phase regressor, and spatial smoothing. The synergy between navigator correction and phase regression reduced geometric distortions better than either step in isolation, and preconditioned the data to make them more amenable to the benefits of spatial smoothing. The combination of these steps provided a 10% increase in t-statistics compared to only navigator correction and spatial smoothing, and reduced the noise and false activations in regions where no legitimate effects would occur.
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