Improving cerebral blood flow quantification for arterial spin labeled perfusion MRI by removing residual motion artifacts and global signal fluctuations

Abstract

Denoising is critical to improving the quality and stability of cerebral blood flow (CBF) quantification in arterial spin labeled (ASL) perfusion magnetic resonance imaging (MRI) due to the intrinsic low signal-to-noise-ratio (SNR) of ASL data. Previous studies have been focused on reducing the spatial or temporal noise using standard filtering techniques, and less attention has been paid to two global nuisance effects, the residual motion artifacts and the global signal fluctuations. Since both nuisances affect the whole brain, removing them in advance should enhance the CBF quantification quality for ASL MRI. The purpose of this paper was to assess this potential benefit. Three methods were proposed to suppress each or both of the two global nuisances. Their performances for CBF quantification were validated using ASL data acquired from 13 subjects. Evaluation results showed that covarying out both global nuisances significantly improved temporal SNR and test-retest stability of CBF measurement. Although the concept of removing both nuisances is not technically novel per se, this paper clearly showed the benefits for ASL CBF quantification. Dissemination of the proposed methods in a free ASL data processing toolbox should be of interest to a broad range of ASL users.