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'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%-48.1% 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 ( ).