Locally Adaptive Low Rank Regularization with Collaborative Data Selection for Arterial Spin Labeling MRI Denoising

Motivation: Address the challenge of low SNR in arterial spin labeling (ASL) MRI that hinders its clinical and research potential. Goal(s): Develop an advanced ASL denoising algorithm that enhances image quality and overcomes limitations in ASL due to low SNR. Approach: Propose a Locally Adaptive low rank regularization with Collaborative data Selection (LACS) scheme that utilizes the structural characteristics of ASL images for collaborative data selection to improve low-rank modeling. The proposed low-rank regularization fundamentally performs locally adaptive PCA without explicit training. Results: Using a single ASL image pair, LACS significantly outperformed state-of-the-art MRI denoising methods and the standard pipeline. Impact: The proposed scheme has the potential to benefit researchers, clinicians, and patients by setting a new benchmark for ASL MRI denoising. It opens doors to exploring ASL's full clinical potential and offers opportunities for innovative research.