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. 2019 Aug 14;10:869. doi: 10.3389/fneur.2019.00869

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Copyright © 2019 Zhu, Jiang, Tong, Xie, Zaharchuk and Wintermark.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Deep learning for improving image quality of arterial spin labeling in a patient with right-sided Moyamoya disease. Reference scan (A) requiring 8 min to collect (nex = 6). Using a rapid scan acquired in 2 min (nex=1) (B), it is possible to create an image (F) with the SNR of a study requiring over 4.5 min (nex = 3) (E). The peak signal-to-noise (PSNR) performance is superior to existing de-noising methods such as (C) block matched 3D (BM3D) and (D) total generalized variation (TGV). Such methods could speed up MRI acquisition, enabling more functional imaging and perhaps reducing the cost of scanning.