Non-contrast Time-resolved 4D MR Angiography Reconstruction using Compressed Sensing with Sparsity Regularization and Subspace Modeling

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.