Non-contrast enhanced 4D MRA has emerged as a promising approach in charactering flow dynamics. The major challenge of conventional 4D MRA is relatively long scan time. Recently developed 4D MRA combining golden-angle stack-of-stars acquisition and compressed sensing reconstruction can significantly shorten scan time. However, this method only exploits spatial sparsity based on individual frames. The current study aims to test the feasibility of a newly developed low-rank and sparsity-based image reconstruction method to highly accelerate 4D MRA. Our initial results suggest that higher acceleration rates can be achieved using GraspMRA without compromising image quality and temporal fidelity.
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