Recent Metal Artifacts Reduction (MAR) methods for Computed Tomography are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves. In this paper, we are exploring the capabilities of a multidomain method, which consists of both sinogram correction (projection domain step) and restored image correction (image-domain step). We formulate the first step problem directly as sinogram inpainting, which allows us to use methods of this specific field, such as partial convolutions. Moreover, we propose a synthetic data generation pipeline to avoid problems with overfitting to metal shapes set and an artifacts formation technique. The proposed method achieves state-of-the-art (-75% MSE) improvement in comparison with a classic benchmark - Li-MAR.