  Metal artifacts caused by the presence of metallic implants tremendously
degrade the reconstructed computed tomography (CT) image quality, affecting
clinical diagnosis or reducing the accuracy of organ delineation and dose
calculation in radiotherapy. Recently, deep learning methods in sinogram and
image domains have been rapidly applied on metal artifact reduction (MAR) task.
The supervised dual-domain methods perform well on synthesized data, while
unsupervised methods with unpaired data are more generalized on clinical data.
However, most existing methods intend to restore the corrupted sinogram within
metal trace, which essentially remove beam hardening artifacts but ignore other
components of metal artifacts, such as scatter, non-linear partial volume
effect and noise. In this paper, we mathematically derive a physical property
of metal artifacts which is verified via Monte Carlo (MC) simulation and
propose a novel physics based non-local dual-domain network (PND-Net) for MAR
in CT imaging. Specifically, we design a novel non-local sinogram decomposition
network (NSD-Net) to acquire the weighted artifact component, and an image
restoration network (IR-Net) is proposed to reduce the residual and secondary
artifacts in the image domain. To facilitate the generalization and robustness
of our method on clinical CT images, we employ a trainable fusion network
(F-Net) in the artifact synthesis path to achieve unpaired learning.
Furthermore, we design an internal consistency loss to ensure the integrity of
anatomical structures in the image domain, and introduce the linear
interpolation sinogram as prior knowledge to guide sinogram decomposition.
Extensive experiments on simulation and clinical data demonstrate that our
method outperforms the state-of-the-art MAR methods.
