Deep learning with domain adaptation for accelerated projection‐reconstruction MR

The radial k‐space trajectory is a well‐established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k‐space trajectory requires a large number of radial lines for high‐resolution reconstruction. Increasing the number of radial lines causes longer acquisition time, making it more difficult for routine clinical use. On the other hand, if we reduce the number of radial lines, streaking artifact patterns are unavoidable. To solve this problem, we propose a novel deep learning approach with domain adaptation to restore high‐resolution MR images from under‐sampled k‐space data.

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