A signal processing model for arterial spin labeling functional MRI
A signal processing model for arterial spin labeling functional MRI
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DOI：
10.1016/j.neuroimage.2004.09.047
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发表时间：
2005-01-01
期刊：
影响因子：
5.7
通讯作者：
Wong, EC
中科院分区：
文献类型：
--
作者：
Liu, TT;Wong, EC
提出了基于动脉自旋标记 (ASL) 的功能磁共振成像 (fMRI) 中的信号路径模型。考虑了三种用于形成灌注估计的基于减法的方法，并将其显示为由调制器和低通滤波器组成的广义估计的特定情况。使用信号模型评估方法的性能。通过在区组设计实验中使用正弦减法或环绕减法以及在随机事件相关实验中使用成对减法，可以最大程度地减少血氧水平相关对比度 (BOLD) 对灌注估计的污染。这些减法方法都倾向于去相关 fMRI 实验中经常观察到的 1/f 型低频噪声。正弦减法提供了低频下最平坦的噪声功率谱，而成对减法则产生最窄的自相关函数。还考虑了从 ASL 数据形成 BOLD 估计，并使用信号模型检查估计的灌注权重。 (C) 2004 Elsevier Inc. 保留所有权利。
A model of the signal path in arterial spin labeling (ASL)-based functional magnetic resonance imaging (fMRI) is presented. Three subtraction-based methods for forming a perfusion estimate are considered and shown to be specific cases of a generalized estimate consisting of a modulator followed by a low pass filter. The performance of the methods is evaluated using the signal model. Contamination of the perfusion estimate by blood oxygenation level dependent contrast (BOLD) is minimized by using either sine subtraction or surround subtraction for block design experiments and by using pair-wise subtraction for randomized event-related experiments. The subtraction methods all tend to decorrelate the 1/f type low frequency noise often observed in fMRI experiments. Sine subtraction provides the flattest noise power spectrum at low frequencies, while pair-wise subtraction yields the narrowest autocorrelation function. The formation of BOLD estimates from the ASL data is also considered and perfusion weighting of the estimates is examined using the signal model. (C) 2004 Elsevier Inc. All rights reserved.