  Recent CT Metal Artifacts Reduction (MAR) methods 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 multi-domain method which consists of both sinogram
correction (projection domain step) and restored image correction (image-domain
step). Moreover, we propose a formulation of the first step problem as sinogram
inpainting which allows us to use methods of this specific field such as
partial convolutions. The proposed method allows to achieve state-of-the-art
(-75% MSE) improvement in comparison with a classic benchmark - Li-MAR.
