Keywords: Sparse & Low-Rank Models, Image Reconstruction
Motivation: ASL-based non-contrast enhanced (non-CE) time-resolved 4D MRA is a promising approach in the diagnosis of cerebrovascular disease, however, it suffers from low SNR and insufficient spatial and temporal resolution.
Goal(s): Our goal was to enhance the quality of 4D non-CE MRA and diminish artifacts.
Approach: This study proposed a novel reconstruct method combining the angiography sparsity and subspace modeling on data acquired by golden-angle stack-of-stars radial pulse sequence.
Results: The performance of the proposed method was compared with NUFFT, conventional GRASP and the state-of-the-art GRASP-pro reconstructions. The results suggest that the proposed method improves the image quality of the 4D ASL-based non-CE MRA.
Impact: The proposed reconstruction method can produce 4D non-CE MRA with high spatial-temporal resolution.
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