Purpose: In this work, we present a workflow to construct generic and robust
generative image priors from magnitude-only images. The priors can then be used
for regularization in reconstruction to improve image quality. Methods: The
workflow begins with the preparation of training datasets from magnitude-only
MR images. This dataset is then augmented with phase information and used to
train generative priors of complex images. Finally, trained priors are
evaluated using both linear and nonlinear reconstruction for compressed sensing
parallel imaging with various undersampling schemes. Results: The results of
our experiments demonstrate that priors trained on complex images outperform
priors trained only on magnitude images. Additionally, a prior trained on a
larger dataset exhibits higher robustness. Finally, we show that the generative
priors are superior to L1 -wavelet regularization for compressed sensing
parallel imaging with high undersampling. Conclusion: These findings stress the
importance of incorporating phase information and leveraging large datasets to
raise the performance and reliability of the generative priors for MRI
reconstruction. Phase augmentation makes it possible to use existing image
databases for training.

本研究提出了一种从仅包含振幅信息的图像中构建通用且稳健的生成图像先验的工作流程，并将其用于改善图像质量的重建中进行正则化。结果表明，使用复杂图像训练的先验表现优于仅使用振幅图像训练的先验，且基于更大数据集训练的先验具有更高的鲁棒性。此外，相较于 L1 小波正则化，生成图像先验在高欠采样条件下的压缩感知并行成像中表现卓越。因此，本研究强调了整合相位信息和利用大型数据集提升 MRI 重建生成图像先验的性能和可靠性的重要性。相位增强使得可以利用现有图像数据库进行训练。