Stable Deep MRI Reconstruction using Generative Priors

Understand

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.