Deep <scp>learning–based</scp> velocity antialiasing of <scp>4D</scp>‐flow <scp>MRI</scp>
Haben Berhane, Michael Scott, Alex J. Barker, Patrick M. McCarthy, Ryan Avery, Brad Allen, Chris Malaisrie, Joshua D. Robinson, Cynthia K. Rigsby, Michael Markl
Abstract
PURPOSE: To develop a convolutional neural network (CNN) for the robust and fast correction of velocity aliasing in 4D-flow MRI. METHODS: This study included 667 adult subjects with aortic 4D-flow MRI data with existing velocity aliasing (n = 362) and no velocity aliasing (n = 305). Additionally, 10 controls received back-to-back 4D-flow scans with systemically varied velocity-encoding sensitivity (vencs) at 60, 100, and 175 cm/s. The no-aliasing data sets were used to simulate velocity aliasing by reducing the venc to 40%-70% of the original, alongside a ground truth locating all aliased voxels (153 training, 152 testing). The 152 simulated and 362 existing aliasing data sets were used for testing and compared with a conventional velocity antialiasing algorithm. Dice scores were calculated to quantify CNN performance. For controls, the venc 175-cm/s scans were used as the ground truth and compared with the CNN-corrected venc 60 and 100 cm/s data sets RESULTS: The CNN required 176 ± 30 s to perform compared with 162 ± 14 s for the conventional algorithm. The CNN showed excellent performance for the simulated data compared with the conventional algorithm (median range of Dice scores CNN: [0.89-0.99], conventional algorithm: [0.84-0.94], p < 0.001, across all simulated vencs) and detected more aliased voxels in existing velocity aliasing data sets (median detected CNN: 159 voxels [31-605], conventional algorithm: 65 [7-417], p < 0.001). For controls, the CNN showed Dice scores of 0.98 [0.95-0.99] and 0.96 [0.87-0.99] for venc = 60 cm/s and 100 cm/s, respectively, while flow comparisons showed moderate-excellent agreement. CONCLUSION: Deep learning enabled fast and robust velocity anti-aliasing in 4D-flow MRI.