Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning

TL;DR

This paper introduces a self-supervised cross-domain deep learning framework for metal artifact reduction in 2D CT images, eliminating the need for paired training data and achieving superior results.

Contribution

The proposed method is the first to use self-supervised learning with cross-domain training for MAR, incorporating metal trace replacement and sinogram-level refinement.

Findings

Outperforms existing MAR methods on simulated and real data

Effectively preserves fine structural details in CT images

Demonstrates potential for various organ sites

Abstract

The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need for anatomically identical CT image pairs (i.e., metal artifact-corrupted CT image and metal artifact-free CT image) for network learning, we propose a self-supervised cross-domain learning framework. Specifically, we train a neural network to restore the metal trace region values in the given metal-free sinogram, where the metal trace is identified by the forward projection of metal masks. We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images. To preserve the fine structure details and fidelity of the final MAR image, instead of directly adopting CNN-refined images as output, we incorporate the metal trace replacement into our framework and replace the metal-affected projections of the original sinogram with the prior sinogram generated by the forward projection of the CNN output. We then use the filtered backward projection (FBP) algorithms for final MAR image reconstruction. We conduct an extensive evaluation on simulated and real artifact data to show the effectiveness of our design. Our method produces superior MAR results and outperforms other compelling methods. We also demonstrate the potential of our framework for other organ sites.