Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting
This addresses the problem of image quality degradation in CT scans for medical imaging, representing an incremental advance over existing methods.
The paper tackled CT metal artifacts reduction by proposing a multi-domain method combining sinogram correction via inpainting with partial convolutions and image-domain correction, achieving a state-of-the-art improvement of -75% MSE compared to the Li-MAR benchmark.
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.