  Purpose: To introduce a novel deep learning method for Robust and Accelerated
Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps
from multi-gradient recalled echo (mGRE) MRI data.
  Methods: RoAR trains a convolutional neural network (CNN) to generate
quantitative R2* maps free from field inhomogeneity artifacts by adopting a
self-supervised learning strategy given (a) mGRE magnitude images, (b) the
biophysical model describing mGRE signal decay, and (c) preliminary-evaluated
F-function accounting for contribution of macroscopic B0 field inhomogeneities.
Importantly, no ground-truth R2* images are required and F-function is only
needed during RoAR training but not application.
  Results: We show that RoAR preserves all features of R2* maps while offering
significant improvements over existing methods in computation speed (seconds
vs. hours) and reduced sensitivity to noise. Even for data with SNR=5 RoAR
produced R2* maps with accuracy of 22% while voxel-wise analysis accuracy was
47%. For SNR=10 the RoAR accuracy increased to 17% vs. 24% for direct
voxel-wise analysis.
  Conclusion: RoAR is trained to recognize the macroscopic magnetic field
inhomogeneities directly from the input magnitude-only mGRE data and eliminate
their effect on R2* measurements. RoAR training is based on the biophysical
model and does not require ground-truth R2* maps. Since RoAR utilizes signal
information not just from individual voxels but also accounts for spatial
patterns of the signals in the images, it reduces the sensitivity of R2* maps
to the noise in the data. These features plus high computational speed provide
significant benefits for the potential usage of RoAR in clinical settings.
