Keywords: Sparse & Low-Rank Models, Arterial spin labelling, Denoising, MRI
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.
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