Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting

TL;DR

This paper introduces a multi-domain CT metal artifact reduction method combining sinogram inpainting with partial convolutions and image correction, achieving state-of-the-art performance with significantly reduced MSE.

Contribution

It presents a novel multi-domain approach that integrates sinogram inpainting using partial convolutions with image correction for improved metal artifact reduction.

Findings

Achieves -75% MSE improvement over Li-MAR

Combines sinogram inpainting and image correction

Utilizes partial convolutions for sinogram inpainting

Abstract

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.