Generative Image Priors for MRI Reconstruction Trained from
  Magnitude-Only Images
Guanxiong Luo Xiaoqing Wang Mortiz Blumenthal Martin Schilling Erik Hans Ulrich Rauf Raviteja Kotikalapudi Niels Focke Martin Uecker
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.