Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping

Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motionand B0-inhomogeneity-corrected R∗ 2 maps from motion-corrupted multiGradient-Recalled Echo (mGRE) MRI data. Methods: We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B0-inhomogeneity-corrected R∗ 2 maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R∗ 2 (and any other mGRE-enabled) maps using the standard voxelwise analysis or machine-learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motionand B0-inhomogeneity-corrected quantitative R∗ 2 maps from motion-corrupted magnitudeonly mGRE images by taking advantage of the biophysical model describing the mGRE signal decay. Results: We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R∗ 2 maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models. Conclusion: Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motionand B0-inhomogeneity-corrected R∗ 2 maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R∗ 2 maps, while LEARN-BIO directly performs motionand B0-inhomogeneity-corrected R∗ 2 estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.