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Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact‐free and denoised R 2 * images
Author(s) -
Torop Max,
Kothapalli Satya V. V. N.,
Sun Yu,
Liu Jiaming,
Kahali Sayan,
Yablonskiy Dmitriy A.,
Kamilov Ulugbek S.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28344
Subject(s) - artificial intelligence , voxel , computer science , ground truth , noise (video) , sensitivity (control systems) , pattern recognition (psychology) , artifact (error) , convolutional neural network , deep learning , signal (programming language) , computation , field (mathematics) , function (biology) , computer vision , algorithm , image (mathematics) , mathematics , electronic engineering , evolutionary biology , pure mathematics , engineering , biology , programming language
Purpose To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B 0‐inhomogeneity‐corrected R 2 * maps from multi‐gradient recalled echo (mGRE) MRI data. Methods RoAR trains a convolutional neural network (CNN) to generate quantitative R 2 ∗ 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 B 0 field inhomogeneities. Importantly, no ground‐truth R 2 * images are required and F‐function is only needed during RoAR training but not application. Results We show that RoAR preserves all features of R 2 * 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 R 2 * 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. Conclusions RoAR is trained to recognize the macroscopic magnetic field inhomogeneities directly from the input magnitude‐only mGRE data and eliminate their effect on R 2 ∗ measurements. RoAR training is based on the biophysical model and does not require ground‐truth R 2 * 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 R 2 * 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.