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Abstract #4774

MUSIC: Multi-Coil Unified Sparsity Regularization Using Inter-Slice Correlation for ASL MRI Denoising

Hangfan Liu1, Bo Li1, Yiran Li1, Manuel Taso2, M. Dylan Tisdall3, Yulin Chang2, John A. Detre3, and Ze Wang1
1University of Maryland School of Medicine, Baltimore, MD, United States, 2Siemens Healthineers, Malvern, PA, United States, 3Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: Signal-to-noise ratio (SNR) is relatively low in arterial spin labeling (ASL) MRI, particularly for high resolution, long post-labeling-delay, or long TE acquisitions.

Goal(s): Increase ASL MRI SNR by leveraging data correlations among multi-channel data.

Approach: Multi-coil Unified Sparsity regularization using Inter-slice Correlation (MUSIC) dynamically suppresses noise by exploiting low-rank properties of inter-slice correlations across channels.

Results: Experimental validation on real-world imaging data demonstrates the efficacy of MUSIC in significantly enhancing ASL perfusion quality compared to existing methods.

Impact: MUSIC enables more accurate perfusion imaging, potentially benefiting neuroimaging diagnostics and encouraging further research into SNR enhancement methods that refine non-invasive imaging across clinical and research settings.

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