Stable Deep MRI Reconstruction using Generative Priors

Zach M, Knoll F, Pock T (2023)

Publication Type: Journal article

Publication year: 2023


Pages Range: 1-1

DOI: 10.1109/TMI.2023.3311345


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.

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Zach, M., Knoll, F., & Pock, T. (2023). Stable Deep MRI Reconstruction using Generative Priors. IEEE Transactions on Medical Imaging, 1-1.


Zach, Martin, Florian Knoll, and Thomas Pock. "Stable Deep MRI Reconstruction using Generative Priors." IEEE Transactions on Medical Imaging (2023): 1-1.

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