  Due to the presence of metallic implants, the imaging quality of computed
tomography (CT) would be heavily degraded. With the rapid development of deep
learning, several network models have been proposed for metal artifact
reduction (MAR). Since the dual-domain MAR methods can leverage the hybrid
information from both sinogram and image domains, they have significantly
improved the performance compared to single-domain methods. However,current
dual-domain methods usually operate on both domains in a specific order, which
implicitly imposes a certain priority prior into MAR and may ignore the latent
information interaction between both domains. To address this problem, in this
paper, we propose a novel interactive dualdomain parallel network for CT MAR,
dubbed as IDOLNet. Different from existing dual-domain methods, the proposed
IDOL-Net is composed of two modules. The disentanglement module is utilized to
generate high-quality prior sinogram and image as the complementary inputs. The
follow-up refinement module consists of two parallel and interactive branches
that simultaneously operate on image and sinogram domain, fully exploiting the
latent information interaction between both domains. The simulated and clinical
results demonstrate that the proposed IDOL-Net outperforms several
state-of-the-art models in both qualitative and quantitative aspects.
