Wang Ze, Aguirre Geoffrey K, Rao Hengyi, Wang Jiongjiong, Fernández-Seara María A, Childress Anna R, Detre John A
Center for Functional Neuroimaging and Department of Neurology, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Magn Reson Imaging. 2008 Feb;26(2):261-9. doi: 10.1016/j.mri.2007.07.003. Epub 2007 Sep 10.
Arterial spin labeling (ASL) perfusion fMRI data differ in important respects from the more familiar blood oxygen level-dependent (BOLD) fMRI data and require specific processing strategies. In this paper, we examined several factors that may influence ASL data analysis, including data storage bit resolution, motion correction, preprocessing for cerebral blood flow (CBF) calculations and nuisance covariate modeling. Continuous ASL data were collected at 3 T from 10 subjects while they performed a simple sensorimotor task with an epoch length of 48 s. These data were then analyzed using systematic variations of the factors listed above to identify the approach that yielded optimal signal detection for task activation. Improvements in statistical power were found for use of at least 10 bits for data storage at 3 T. No significant difference was found in motor cortex regarding using simple subtraction or sinc subtraction, but the former presented minor but significantly (P<.024) larger peak t value in visual cortex. While artifactual head motion patterns were observed in synthetic data and background-suppressed ASL data when label/control images were realigned to a common target, independent realignment of label and control images did not yield significant improvements in activation in the sensorimotor data. It was also found that CBF calculations should be performed prior to spatial normalization and that modeling of global fluctuations yielded significantly increased peak t value in motor cortex. The implementation of all ASL data processing approaches is easily accomplished within an open-source toolbox, ASLtbx, and is advocated for most perfusion fMRI data sets.
动脉自旋标记（ASL）灌注功能磁共振成像（fMRI）数据在重要方面与更为人熟知的血氧水平依赖（BOLD）fMRI数据不同，需要特定的处理策略。在本文中，我们研究了几个可能影响ASL数据分析的因素，包括数据存储位分辨率、运动校正、脑血流量（CBF）计算的预处理以及干扰协变量建模。在3T条件下，从10名受试者收集连续ASL数据，同时他们执行一个时长为48秒的简单感觉运动任务。然后使用上述因素的系统变化对这些数据进行分析，以确定能为任务激活产生最佳信号检测的方法。发现在3T条件下使用至少10位进行数据存储可提高统计功效。在运动皮层，使用简单减法或正弦减法未发现显著差异，但在视觉皮层，前者呈现出较小但显著（P<0.024）更大的峰值t值。当标记/对照图像重新对齐到一个共同目标时，在合成数据和背景抑制的ASL数据中观察到人为的头部运动模式，而标记和对照图像的独立重新对齐在感觉运动数据的激活方面未产生显著改善。还发现CBF计算应在空间归一化之前进行，并且全局波动建模在运动皮层产生了显著增加的峰值t值。所有ASL数据处理方法的实现都可以在一个开源工具箱ASLtbx中轻松完成，并且推荐用于大多数灌注fMRI数据集。
