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In-scanner motion degrades the quality of magnetic resonance imaging (MRI) thereby reducing its utility in the detection of clinically relevant abnormalities. We collaborate with doctors from NYU Langone&#x0027;s Comprehensive Epilepsy Center and apply a deep learning-based MRI artifact reduction model (DMAR) to correct head motion artifacts in brain MRI scans. Specifically, DMAR employs a two-stage approach: in the first, degraded regions are detected using the Single Shot Multibox Detector (SSD), and in the second, the artifacts within the found regions are reduced using a convolutional autoencoder (CAE). We further introduce a set of novel data augmentation techniques to address the high dimensionality of MRI images and the scarcity of available data. As a result, our model was trained on a large synthetic dataset of 225,000 images generated using 375 whole brain T1-weighted MRI scans from the OASIS-1 dataset. DMAR visibly reduces image artifacts when validated using real-world artifact-affected scans from the multi-center ABIDE study and proprietary data collected at NYU. Quantitatively, depending on the level of degradation, our model achieves a 27.8&#x0025;-48.1&#x0025; reduction in RMSE and a 2.88-5.79 dB gain in PSNR on a 5000-sample set of synthetic images. For real-world data without ground-truth, our model reduced the variance of image voxel intensity within artifact-affected brain regions (<tex>$\mathrm{p}=0.014$</tex>).

Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques