Purpose: 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.
Methods: The proposed deep network removes the streaking artifacts from the
artifact corrupted images. To address the situation given the limited available
data, we propose a domain adaptation scheme that employs a pre-trained network
using a large number of x-ray computed tomography (CT) or synthesized radial MR
datasets, which is then fine-tuned with only a few radial MR datasets.
Results: The proposed method outperforms existing compressed sensing
algorithms, such as the total variation and PR-FOCUSS methods. In addition, the
calculation time is several orders of magnitude faster than the total variation
and PR-FOCUSS methods.Moreover, we found that pre-training using CT or MR data
from similar organ data is more important than pre-training using data from the
same modality for different organ.
Conclusion: We demonstrate the possibility of a domain-adaptation when only a
limited amount of MR data is available. The proposed method surpasses the
existing compressed sensing algorithms in terms of the image quality and
computation time.

本文提出了一种结合深度学习和领域自适应的新方法，使用大量计算机断层扫描或合成径向磁共振数据预先训练网络，通过少量径向磁共振数据进行微调，成功地从欠采样的 k 空间数据中还原高分辨率的磁共振图像。使用该方法能够优于现有的压缩感知算法，并且计算时间比总变差和 PR-FOCUSS 方法快几个数量级。同时，本研究还发现，使用来自相似器官数据的 CT 或 MR 数据进行预训练比使用来自不同器官的相同模态的数据进行预训练更重要。