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Purpose: 3D Time-of-flight (TOF) MR Angiography (MRA) can accuratelyvisualize the intracranial vasculature, but is limited by long acquisitiontimes. Compressed sensing (CS) reconstruction can be used to substantiallyaccelerate acquisitions. The quality of those reconstructions depends on theundersampling patterns used in the acquisitions. In this work, optimized setsof undersampling parameters using various acceleration factors for Cartesian 3DTOF-MRA are established.  Methods: Fully-sampled datasets acquired at 7T were retrospectivelyundersampled using variable-density Poisson-disk sampling with variousautocalibration region sizes, polynomial orders, and acceleration factors. Theaccuracy of reconstructions from the different undersampled datasets wasassessed using the vessel-masked structural similarity index. Results werecompared for four imaging volumes, acquired from two different subjects.Optimized undersampling parameters were validated using additionalprospectively undersampled datasets.  Results: For all acceleration factors, using a fully-sampled calibration areaof 12x12 k-space lines and a polynomial order of around 2-2.4 resulted in thehighest image quality. The importance of sampling parameter optimization wasfound to increase for higher acceleration factors. The results were consistentacross resolutions and regions of interest with vessels of varying sizes andtortuosity. In prospectively undersampled acquisitions, using optimizedundersampling parameters resulted in a 7.2% increase in the number of visiblesmall vessels at R = 7.2.  Conclusion: The image quality of CS TOF-MRA can be improved by appropriatechoice of undersampling parameters. The optimized sets of parameters areindependent of the acceleration factor.

Optimization of Undersampling Parameters for 3D Intracranial Compressed Sensing MR Angiography at 7 Tesla