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PURPOSE: 3D time-of-flight MRA can accurately visualize the intracranial vasculature but is limited by long acquisition times. Compressed sensing reconstruction can be used to substantially accelerate acquisitions. The quality of those reconstructions depends on the undersampling patterns used. In this work, we optimize sets of undersampling parameters for various acceleration factors of Cartesian 3D time-of-flight MRA. METHODS: Fully sampled datasets, acquired at 7 Tesla, were retrospectively undersampled using variable-density Poisson disk sampling with various autocalibration region sizes, polynomial orders, and acceleration factors. The accuracy of reconstructions from the different undersampled datasets was assessed using the vessel-masked structural similarity index. Identified optimal undersampling parameters were then evaluated in additional prospectively undersampled datasets. Compressed sensing reconstruction parameters were chosen based on a preliminary reconstruction parameter optimization. RESULTS: For all acceleration factors, using a fully sampled calibration area of 12 × 12 k-space lines and a polynomial order of 2 resulted in the highest image quality. The importance of parameter optimization of the sampling was found to increase for higher acceleration factors. The results were consistent across resolutions and regions of interest with vessels of varying sizes and tortuosity. The number of visible small vessels increased by 7.0% and 14.2% when compared to standard parameters for acceleration factors of 7.2 and 15, respectively. CONCLUSION: The image quality of compressed sensing time-of-flight MRA can be improved by appropriate choice of undersampling parameters. The optimized sets of parameters are independent of the acceleration factor and enable a larger number of vessels to be visualized.

Original publication




Journal article


Magn Reson Med

Publication Date





880 - 889


MR angiography, compressed sensing, lenticulostriate arteries, time-of-flight MRA, ultra-high field, undersampling, Acceleration, Algorithms, Image Processing, Computer-Assisted, Magnetic Resonance Angiography, Retrospective Studies