In this study, the problem of motion-based artifact reduction in magnetic resonance imaging scans is considered. Motion-based artifacts caused by the patient’s body movements during the examination. The solution of the problem proposed in this work is based on deep learning methods directly applied to scans. The specific model used in our investigation was a Wesserstein generative adversarial network. The first module of the applied model was a discriminator that was used to classify real or fake scans. The second module serves as a generator and is used to produce fake scans. These models are trained iteratively with the use of ADAM optimizer which is an extension of stochastic gradient descent. The model was trained with 100 motion-free T1 scans with simulated motion artifacts. The corrected scans were evaluated using structural image similarity (SSIM), mean square error (MSE), and peak signal-to-noise ratio (PSNR), and the measures were compared with the scans without motion artifacts. In our results, we observed an improvement in the corrected scans for each of the measures used.