Non-linear fitting with joint spatial regularization in Arterial Spin Labeling

TL;DR

This paper introduces a joint spatial regularization method for non-linear fitting in arterial spin labeling imaging, significantly improving SNR and map sharpness in cerebral blood flow and arterial transit time estimates, especially in low SNR regions.

Contribution

It proposes a novel joint spatial total generalized variation regularization technique for ASL data fitting, enhancing accuracy and noise suppression over existing methods.

Findings

Outperforms non-linear least squares and Bayesian methods in SNR improvement.

Maintains map sharpness and quantitative accuracy in synthetic, healthy, and stroke data.

Effective in low SNR regions, improving clinical and research ASL imaging.

Abstract

Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.