Fourier-based arterial spin labeling (ASL) data analysis robust against abrupt and periodic artifacts

Motivation: ASL data has distinct feature in Fourier domain, potentially enabling development of Fourier-based ASL data analysis. Goal(s): To introduce a Fourier-based method to analyze ASL data including fMRI data and verify the robustness to abrupt and periodic artifacts. Approach: Contribution of the abrupt artifacts in the perfusion frequency component was estimated and eliminated to recover the perfusion signal. For the robustness to the periodic artifacts, weighted regression was applied for correlation calculation in the ASL fMRI analysis. Results: The Fourier-based ASL analysis yielded higher SNR in abrupt artifacts and more robust fMRI maps with periodic artifacts. Impact: The proposed Fourier-based ASL analysis method is robust to various artifacts, yielding higher SNR and more robust fMRI maps. The study demonstrated for the first time that ASL perfusion fMRI data can be analyzed in Fourier domain, providing new perspectives.