Reference-Superimposed Reconstruction (RS-Recon) for Arterial Spin Labeling

Conventional arterial spin labeling (ASL) reconstruction suffers from sign-flipping errors and/or noise bias under strong background suppression (BS), limiting SNR optimization. We propose reference-superimposed reconstruction (RS-recon) to enable ideal BS and robust signal recovery.

RS-recon superimposes high-SNR M₀ k-space data onto ASL k-space before reconstruction, with subsequent reference subtraction. Four volunteers underwent 3T scanning with pulsed ASL, pseudo-continuous ASL, and dual-module velocity-selective ASL across varying BS levels. RS-recon integrated reconstruction pipelines were compared to standard magnitude (Mag), complex subtraction (ComplexSub), and complex (Complex) reconstruction pipelines. The performance was evaluated by metrics including ASL signal, temporal SNR (tSNR), artifacts, misalignment robustness, and generalized auto-calibrating partially parallel acquisitions (GRAPPA) compatibility.

RS-recon eliminated subtraction artifacts due to signal sign-flipping, recovered negative ASL signals, and enabled ideal BS for maximizing SNR. It significantly improved ASL signal and tSNR in gray/white matter across all labeling methods and different BS conditions. RS-recon corrected phase errors under low-SNR conditions, preserved signal signs, maintained Gaussian noise distribution, remained robust against misalignments between the reference and the ASL images, and integrated well with GRAPPA. Mag + RS performance matched complex-based (ComplexSub and Complex) RS pipelines and is recommended for its simplicity, compatibility, and robustness overall.

RS-recon offers a simple, robust strategy for ASL image reconstruction, resolving subtraction errors even with magnitude output and enabling optimal background suppression. It enhances signal fidelity, SNR, and phase accuracy while integrating seamlessly with existing reconstruction pipelines and parallel imaging.