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Purpose: To introduce a novel deep learning method for Robust and AcceleratedReconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* mapsfrom multi-gradient recalled echo (mGRE) MRI data.  Methods: RoAR trains a convolutional neural network (CNN) to generatequantitative R2* maps free from field inhomogeneity artifacts by adopting aself-supervised learning strategy given (a) mGRE magnitude images, (b) thebiophysical model describing mGRE signal decay, and (c) preliminary-evaluatedF-function accounting for contribution of macroscopic B0 field inhomogeneities.Importantly, no ground-truth R2* images are required and F-function is onlyneeded during RoAR training but not application.  Results: We show that RoAR preserves all features of R2* maps while offeringsignificant improvements over existing methods in computation speed (secondsvs. hours) and reduced sensitivity to noise. Even for data with SNR=5 RoARproduced R2* maps with accuracy of 22% while voxel-wise analysis accuracy was47%. For SNR=10 the RoAR accuracy increased to 17% vs. 24% for directvoxel-wise analysis.  Conclusion: RoAR is trained to recognize the macroscopic magnetic fieldinhomogeneities directly from the input magnitude-only mGRE data and eliminatetheir effect on R2* measurements. RoAR training is based on the biophysicalmodel and does not require ground-truth R2* maps. Since RoAR utilizes signalinformation not just from individual voxels but also accounts for spatialpatterns of the signals in the images, it reduces the sensitivity of R2* mapsto the noise in the data. These features plus high computational speed providesignificant benefits for the potential usage of RoAR in clinical settings.

Deep learning using a biophysical model for Robust and Accelerated Reconstruction (RoAR) of quantitative and artifact-free R2* images