Fast Multiparametric ASL MRI via Multi-Dimensional Acceleration and Subspace Reconstruction

Multi-post-labeling delays (PLDs) multi-echo times (TEs) arterial spin labeling (ASL) MRI enables simultaneous quantification of cerebral blood flow (CBF), arterial transit time (ATT), and blood-brain barrier (BBB) water exchange, but requires long scan times due to the combination of multi-TEs, PLDs, and repeated control-label acquisitions. A five-dimensional (5D) acquisition and reconstruction framework was developed to enable accelerated multi-parametric ASL.

A coordinated undersampling strategy was designed across in-plane k-space, through-plane k-space, TE, and control-label measurement dimensions, achieving a combined acceleration factor of approximately 28. Reconstruction was performed using a Kronecker-structured subspace model that exploited low-rank correlations along the TE and control-label dimensions. Sparsity regularization was applied in the subspace coefficient domain, and temporal bases were estimated from a single fully sampled middle slice. The framework was evaluated using in vivo echo-time-resolved ASL (TASL) datasets from eight subjects.

The proposed reconstruction outperformed a matched compressed sensing (CS) reconstruction across all echo times, yielding higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), with the largest improvements observed under severe undersampling (R≈28) and at late echo times. Higher-fidelity CBF, ATT, BBB, and intravascular transit time (ITT) maps were obtained, with the greatest gains observed for BBB and ITT. Test-retest reliability, assessed using intraclass correlation coefficient (ICC), remained comparable to fully sampled reference data.

Coordinated multi-dimensional undersampling combined with Kronecker subspace reconstruction enables highly accelerated multi-parametric ASL while preserving image quality, quantitative parameter estimation, and test-retest reliability, supporting clinically feasible acquisition times.