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Purpose To develop and evaluate a deep learning-based method (MC-Net) tosuppress motion artifacts in brain magnetic resonance imaging (MRI). MethodsMC-Net was derived from a UNet combined with a two-stage multi-loss function.T1-weighted axial brain images contaminated with synthetic motions were used totrain the network. Evaluation used simulated T1 and T2-weighted axial, coronal,and sagittal images unseen during training, as well as T1-weighted images withmotion artifacts from real scans. Performance indices included the peak signalto noise ratio (PSNR), structural similarity index measure (SSIM), and visualreading scores. Two clinical readers scored the images. Results The MC-Netoutperformed other methods implemented in terms of PSNR and SSIM on the T1axial test set. The MC-Net significantly improved the quality of allT1-weighted images (for all directions and for simulated as well as real motionartifacts), both on quantitative measures and visual scores. However, theMC-Net performed poorly on images of untrained contrast (T2-weighted).Conclusion The proposed two-stage multi-loss MC-Net can effectively suppressmotion artifacts in brain MRI without compromising image context. Given theefficiency of the MC-Net (single image processing time ~40ms), it canpotentially be used in real clinical settings. To facilitate further research,the code and trained model are available athttps://github.com/MRIMoCo/DL_Motion_Correction.

Motion correction in MRI using deep learning and a novel hybrid loss function