An advanced reconstruction framework in stack-of-stars golden-angle radial non-contrast enhanced 4D MRA with ultra-high temporal resolution

In the present study, we developed an advanced reconstruction framework for ASL-based 4-dimensional (4D) MRA dubbed GraspMRA, which combines stack-of-stars golden-angle radial sampling with low-rank subspace-based image reconstruction to achieve ultra-high temporal resolution. The performance of GraspMRA was evaluated by comparison with three other reconstruction methods at different acceleration rates. Our results have demonstrated that GraspMRA has superior performance to the other methods, and it provides real flow dynamics at an ultra-high temporal resolution of up to 25ms per 3D volume while preserving good image quality.