Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data. Embedding the trained model in a classical variational approach yields high-quality reconstructions irrespective of the sub-sampling pattern. In addition, the model shows stable behavior when confronted with out-of-distribution data in the form of contrast variation. Furthermore, a probabilistic interpretation provides a distribution of reconstructions and hence allows uncertainty quantification. To reconstruct parallel MRI, we propose a fast algorithm to jointly estimate the image and the sensitivity maps. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods, while preserving the flexibility with respect to sub-sampling patterns and allowing for uncertainty quantification.

翻译：数据驱动方法近年来在磁共振成像（MRI）重建中取得了显著成功，但由于缺乏泛化性和可解释性，将其整合到临床常规中仍具挑战。本文基于生成图像先验的统一框架应对这些挑战。我们提出一种新型深度神经网络正则化器，该正则化器在生成式设置下仅基于参考幅度图像进行训练。训练后，该正则化器编码了更高层次的领域统计特征——我们在无数据条件下通过合成图像验证了这一点。将训练好的模型嵌入经典变分方法中，无论子采样模式如何，均可获得高质量重建结果。此外，该模型在面对对比度变化形式的分布外数据时表现出稳定行为。同时，基于概率解释可提供重建结果的分布，从而实现不确定性量化。为重建并行MRI，我们提出一种联合估计图像与灵敏度图的快速算法。结果表明，该方法在保持子采样模式灵活性的同时，实现了与最先进端到端深度学习方法相当的性能，并支持不确定性量化。