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 a 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.

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