  Metal implants and other high-density objects in patients introduce severe
streaking artifacts in CT images, compromising image quality and diagnostic
performance. Although various methods were developed for CT metal artifact
reduction over the past decades, including the latest dual-domain deep
networks, remaining metal artifacts are still clinically challenging in many
cases. Here we extend the state-of-the-art dual-domain deep network approach
into a quad-domain counterpart so that all the features in the sinogram, image,
and their corresponding Fourier domains are synergized to eliminate metal
artifacts optimally without compromising structural subtleties. Our proposed
quad-domain network for MAR, referred to as Quad-Net, takes little additional
computational cost since the Fourier transform is highly efficient, and works
across the four receptive fields to learn both global and local features as
well as their relations. Specifically, we first design a Sinogram-Fourier
Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to
faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an
Image-Fourier Refinement Network (IFR-Net) which takes both an image and its
Fourier spectrum to improve a CT image reconstructed from the SFR-Net output
using cross-domain contextual information. Quad-Net is trained on clinical
datasets to minimize a composite loss function. Quad-Net does not require
precise metal masks, which is of great importance in clinical practice. Our
experimental results demonstrate the superiority of Quad-Net over the
state-of-the-art MAR methods quantitatively, visually, and statistically. The
Quad-Net code is publicly available at
https://github.com/longzilicart/Quad-Net.
