Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx.

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.