Highly accelerated vessel-selective arterial spin labeling angiography using sparsity and smoothness constraints.

PURPOSE: To demonstrate that vessel selectivity in dynamic arterial spin labeling angiography can be achieved without any scan-time penalty or noticeable loss of image quality compared with conventional arterial spin labeling angiography. METHODS: Simulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared with non-vessel-encoded angiograms alone can improve reconstruction results in a compressed-sensing framework. Further simulations were performed to study whether the difference in relative sparsity between nonselective and vessel-selective dynamic angiograms was sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden-angle radial trajectory and reconstructed by enforcing image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed-sensing framework. RESULTS: Relative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and nonselective angiography were negligible compared with the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction. CONCLUSION: The increase in relative sparsity of vessel-selective angiograms compared with nonselective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan-time cost.