Adaptive Joint Data Selection for Sparsity Based Arterial Spin Labeling MRI Denoising

Arterial spin-labeled (ASL) perfusion MRI remains the only non-invasive, radiation-free method for quantifying regional tissue perfusion. ASL MRI computes perfusion signals from the difference of the spin-labeled images and spin-untagged control images. Limited by the T1 decay of the labeled arterial blood, ASL MRI signal is subject to a low signal-to-noise ratio. This issue is particularly vexing due to the absence of ground truth and the difficulty in preserving image textures amidst substantial noise reduction efforts. One major avenue for tackling this challenge involves leveraging the sparsity of image signals, a technique widely employed in unsupervised image denoising. Compared to global models operating at the slice level, enhanced local sparse models not only improve the separation of signal from noise but also preserves local structures more effectively. This paper introduces a joint data selection strategy tailored for ASL denoising, which capitalizes on the strong correlation between paired label and control (L/C) images to identify and assemble highly correlated content, forming potentially sparse matrices. The application of sparsity regularization to these matrices is inherently more adaptive to local structures. Crucially, the proposed method does not rely on any ground-truth training data. In real-world testing with an ASL MRI dataset, the proposed approach remarkably enhances the quality of ASL perfusion maps, uti-lizing only a single pair of L/C images, and outperforms the conventional pipeline that necessitates multiple L/C pairs.