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.

翻译：目的：本研究提出了一种从幅度图像构建通用且鲁棒的生成图像先验的工作流程，这些先验可进一步用于重建过程中的正则化以提升图像质量。方法：该工作流程首先从幅度MR图像中准备训练数据集，随后对该数据集进行相位信息增强，并用于训练复图像生成先验。最后，通过线性与非线性重建方法，结合各种欠采样方案对压缩感知并行成像中的先验进行评估。结果：实验结果表明，基于复图像训练的先验优于仅基于幅度图像训练的先验。此外，在更大数据集上训练的先验表现出更高的鲁棒性。最终，我们证明在高欠采样压缩感知并行成像中，生成先验优于L1-小波正则化。结论：这些发现强调了整合相位信息并利用大数据集对提升MRI重建中生成先验的性能与可靠性的重要性。相位增强技术使现有图像数据库用于训练成为可能。