Generative priors for MRI reconstruction trained from magnitude-only images using phase augmentation.

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. The workflow begins with the preparation of training datasets from magnitude-only magnetic resonance (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. The results of our experiments demonstrate that priors trained on complex images outperform priors trained only on magnitude images. In addition, a prior trained on a larger dataset exhibits higher robustness. Finally, we show that the generative priors are superior to [Formula: see text]-wavelet regularization for compressed sensing parallel imaging with high undersampling. These findings stress the importance of incorporating phase information and leveraging large datasets to raise the performance and reliability of the generative priors for MR imaging (MRI) reconstruction. Phase augmentation makes it possible to use existing image databases for training.This article is part of the theme issue 'Generative modelling meets Bayesian inference: a new paradigm for inverse problems'.